#install.packages('TDAmapper')
library(TDAmapper)
library(cluster)
library(vip)
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#install.packages('kernlab’)
library(kernlab)
#install.packages(‘class’)
library(class)
#install.packages('nnet')
library(nnet)
#install.packages(‘randomForest’)
library(randomForest)
## randomForest 4.7-1.1
## Type rfNews() to see new features/changes/bug fixes.
#install.packages('e1071')
library(e1071)                                                  
#install.packages("BayesFactor")
library(BayesFactor)
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## ************
## Welcome to BayesFactor 0.9.12-4.5. If you have questions, please contact Richard Morey (richarddmorey@gmail.com).
## 
## Type BFManual() to open the manual.
## ************
library(BayesPPD)
library(bayestestR)
#install.packages('igraph')
library('igraph')
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#install.packages('locfit')
library(locfit)
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#install.packages('networkD3')
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library(rstanarm)
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## - Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
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library(see)
#install.packages('tidyverse')
library(tidyverse)
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#install.packages('caret')
library(caret)
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## ## Copyright (C) 2003-2025 Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park
## ##
## ## Support provided by the U.S. National Science Foundation
## ## (Grants SES-0350646 and SES-0350613)
## ##
#linstall.packages("caret")
library(caret)
library(TDA)
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library(ks)
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#install.packages('googledrive')
library(googledrive)
#install.packages('stringr')
library(stringr)
#install.packages('ks')
library(ks)
library(GGally)
## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2
#import DryBean dataset from UCI repository stored on my desktop

#Dry_Bean_Dataset **
library(readxl)
Dry_Bean_Dataset <- read_excel("~/Desktop/NCU/DissertationDatasets/DryBeanDataset/Dry_Bean_Dataset.xlsx")
  head(str(Dry_Bean_Dataset))
## tibble [13,611 × 17] (S3: tbl_df/tbl/data.frame)
##  $ Area           : num [1:13611] 28395 28734 29380 30008 30140 ...
##  $ Perimeter      : num [1:13611] 610 638 624 646 620 ...
##  $ MajorAxisLength: num [1:13611] 208 201 213 211 202 ...
##  $ MinorAxisLength: num [1:13611] 174 183 176 183 190 ...
##  $ AspectRation   : num [1:13611] 1.2 1.1 1.21 1.15 1.06 ...
##  $ Eccentricity   : num [1:13611] 0.55 0.412 0.563 0.499 0.334 ...
##  $ ConvexArea     : num [1:13611] 28715 29172 29690 30724 30417 ...
##  $ EquivDiameter  : num [1:13611] 190 191 193 195 196 ...
##  $ Extent         : num [1:13611] 0.764 0.784 0.778 0.783 0.773 ...
##  $ Solidity       : num [1:13611] 0.989 0.985 0.99 0.977 0.991 ...
##  $ roundness      : num [1:13611] 0.958 0.887 0.948 0.904 0.985 ...
##  $ Compactness    : num [1:13611] 0.913 0.954 0.909 0.928 0.971 ...
##  $ ShapeFactor1   : num [1:13611] 0.00733 0.00698 0.00724 0.00702 0.0067 ...
##  $ ShapeFactor2   : num [1:13611] 0.00315 0.00356 0.00305 0.00321 0.00366 ...
##  $ ShapeFactor3   : num [1:13611] 0.834 0.91 0.826 0.862 0.942 ...
##  $ ShapeFactor4   : num [1:13611] 0.999 0.998 0.999 0.994 0.999 ...
##  $ Class          : chr [1:13611] "SEKER" "SEKER" "SEKER" "SEKER" ...
## NULL
  summary(Dry_Bean_Dataset)
##       Area          Perimeter      MajorAxisLength MinorAxisLength
##  Min.   : 20420   Min.   : 524.7   Min.   :183.6   Min.   :122.5  
##  1st Qu.: 36328   1st Qu.: 703.5   1st Qu.:253.3   1st Qu.:175.8  
##  Median : 44652   Median : 794.9   Median :296.9   Median :192.4  
##  Mean   : 53048   Mean   : 855.3   Mean   :320.1   Mean   :202.3  
##  3rd Qu.: 61332   3rd Qu.: 977.2   3rd Qu.:376.5   3rd Qu.:217.0  
##  Max.   :254616   Max.   :1985.4   Max.   :738.9   Max.   :460.2  
##   AspectRation    Eccentricity      ConvexArea     EquivDiameter  
##  Min.   :1.025   Min.   :0.2190   Min.   : 20684   Min.   :161.2  
##  1st Qu.:1.432   1st Qu.:0.7159   1st Qu.: 36714   1st Qu.:215.1  
##  Median :1.551   Median :0.7644   Median : 45178   Median :238.4  
##  Mean   :1.583   Mean   :0.7509   Mean   : 53768   Mean   :253.1  
##  3rd Qu.:1.707   3rd Qu.:0.8105   3rd Qu.: 62294   3rd Qu.:279.4  
##  Max.   :2.430   Max.   :0.9114   Max.   :263261   Max.   :569.4  
##      Extent          Solidity        roundness       Compactness    
##  Min.   :0.5553   Min.   :0.9192   Min.   :0.4896   Min.   :0.6406  
##  1st Qu.:0.7186   1st Qu.:0.9857   1st Qu.:0.8321   1st Qu.:0.7625  
##  Median :0.7599   Median :0.9883   Median :0.8832   Median :0.8013  
##  Mean   :0.7497   Mean   :0.9871   Mean   :0.8733   Mean   :0.7999  
##  3rd Qu.:0.7869   3rd Qu.:0.9900   3rd Qu.:0.9169   3rd Qu.:0.8343  
##  Max.   :0.8662   Max.   :0.9947   Max.   :0.9907   Max.   :0.9873  
##   ShapeFactor1       ShapeFactor2        ShapeFactor3     ShapeFactor4   
##  Min.   :0.002778   Min.   :0.0005642   Min.   :0.4103   Min.   :0.9477  
##  1st Qu.:0.005900   1st Qu.:0.0011535   1st Qu.:0.5814   1st Qu.:0.9937  
##  Median :0.006645   Median :0.0016935   Median :0.6420   Median :0.9964  
##  Mean   :0.006564   Mean   :0.0017159   Mean   :0.6436   Mean   :0.9951  
##  3rd Qu.:0.007271   3rd Qu.:0.0021703   3rd Qu.:0.6960   3rd Qu.:0.9979  
##  Max.   :0.010451   Max.   :0.0036650   Max.   :0.9748   Max.   :0.9997  
##     Class          
##  Length:13611      
##  Class :character  
##  Mode  :character  
##                    
##                    
## 
  ggpairs(Dry_Bean_Dataset, aes(color = Class))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

  ggpairs(Dry_Bean_Dataset, columns = c(1:8,17), aes(color = Class))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

  ggpairs(Dry_Bean_Dataset, columns = c(9:17), aes(color = Class))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

##Add Bayesian tests functions

#create function to conduct the Bayesian Sign Test
BayesianSignTest <- function(diffVector,rope_min,rope_max) {

  library(MCMCpack)

  samples <- 3000

  #build the vector 0.5 1 1 ....... 1 

  weights <- c(0.5,rep(1,length(diffVector)))

  #add the fake first observation in 0

  diffVector <- c (0, diffVector)  


  #for the moment we implement the sign test. Signedrank will follows

  probLeft <- mean (diffVector < rope_min)

  probRope <- mean (diffVector > rope_min & diffVector < rope_max)

  probRight <- mean (diffVector > rope_max)

  results = list ("probLeft"=probLeft, "probRope"=probRope,
                  
                  "probRight"=probRight)
  
  return (results)
}


##Create function to conduct Bayesian Signed Rank Test

BayesianSignedRank <- function(diffVector,rope_min,rope_max) {
  
  library(MCMCpack)
  
  samples <- 30000
  
  #build the vector 0.5 1 1 ....... 1
  weights <- c(0.5,rep(1,length(diffVector)))
  
  #add the fake first observation in 0
  diffVector <- c (0, diffVector)
  
  sampledWeights <- rdirichlet(samples,weights)
  
  winLeft <- vector(length = samples)
  winRope <- vector(length = samples)
  winRight <- vector(length = samples)
  
  for (rep in 1:samples){
    currentWeights <- sampledWeights[rep,]
    for (i in 1:length(currentWeights)){
      for (j in 1:length(currentWeights)){
        product= currentWeights[i] * currentWeights[j]
        if (diffVector[i]+diffVector[j] > (2*rope_max) ) {
          winRight[rep] <- winRight[rep] + product
        }
        else if (diffVector[i]+diffVector[j] > (2*rope_min) ) {
          winRope[rep] <- winRope[rep] + product
        }
        else {
          winLeft[rep] <- winLeft[rep] + product
        }

      }
    }
    maxWins=max(winRight[rep],winRope[rep],winLeft[rep])
    winners = (winRight[rep]==maxWins)*1 + (winRope[rep]==maxWins)*1 + (winLeft[rep]==maxWins)*1
    winRight[rep] <- (winRight[rep]==maxWins)*1/winners
    winRope[rep] <- (winRope[rep]==maxWins)*1/winners
    winLeft[rep] <- (winLeft[rep]==maxWins)*1/winners
  }
  
  
  results = list ("winLeft"=mean(winLeft), "winRope"=mean(winRope),
                  "winRight"=mean(winRight) )
  return (results)
  
}


#Create function to conduct the Bayesian Correlated t.test

#diff_a_b is a vector of differences between the two classifiers, on each fold of cross-validation.
#If you have done 10 runs of 10-folds cross-validation, you have 100 results for each classifier.
#You should have run cross-validation on the same folds for the two classifiers.
#Then diff_a_b is the difference fold-by-fold.

#rho is the correlation of the cross-validation results: 1/(number of folds)
#rope_min and rope_max are the lower and the upper bound of the rope
 
correlatedBayesianTtest <- function(diff_a_b,rho,rope_min,rope_max){
   if (rope_max < rope_min){
     stop("rope_max should be larger than rope_min")
   }
     
  delta <- mean(diff_a_b)
  n <- length(diff_a_b)
  df <- n-1
  stdX <- sd(diff_a_b)
  sp <- sd(diff_a_b)*sqrt(1/n + rho/(1-rho))
  p.left <- pt((rope_min - delta)/sp, df)
  p.rope <- pt((rope_max - delta)/sp, df)-p.left
  results <- list('left'=p.left,'rope'=p.rope,'right'=1-p.left-p.rope)
  return (results)
}
set.seed(16974)
###Prepare drybean dataset for One hot encoding if necessary and Persistent homology.
##One hot encoding for drybean dataset
library(caret)

#define one-hot encoding function
dummy_drybean <- dummyVars(" ~ .", data=Dry_Bean_Dataset)

#perform one-hot encoding on data frame
dry_bean_dataset_one_hot_df <- data.frame(predict(dummy_drybean, newdata=Dry_Bean_Dataset))
summary(dry_bean_dataset_one_hot_df)
##       Area          Perimeter      MajorAxisLength MinorAxisLength
##  Min.   : 20420   Min.   : 524.7   Min.   :183.6   Min.   :122.5  
##  1st Qu.: 36328   1st Qu.: 703.5   1st Qu.:253.3   1st Qu.:175.8  
##  Median : 44652   Median : 794.9   Median :296.9   Median :192.4  
##  Mean   : 53048   Mean   : 855.3   Mean   :320.1   Mean   :202.3  
##  3rd Qu.: 61332   3rd Qu.: 977.2   3rd Qu.:376.5   3rd Qu.:217.0  
##  Max.   :254616   Max.   :1985.4   Max.   :738.9   Max.   :460.2  
##   AspectRation    Eccentricity      ConvexArea     EquivDiameter  
##  Min.   :1.025   Min.   :0.2190   Min.   : 20684   Min.   :161.2  
##  1st Qu.:1.432   1st Qu.:0.7159   1st Qu.: 36714   1st Qu.:215.1  
##  Median :1.551   Median :0.7644   Median : 45178   Median :238.4  
##  Mean   :1.583   Mean   :0.7509   Mean   : 53768   Mean   :253.1  
##  3rd Qu.:1.707   3rd Qu.:0.8105   3rd Qu.: 62294   3rd Qu.:279.4  
##  Max.   :2.430   Max.   :0.9114   Max.   :263261   Max.   :569.4  
##      Extent          Solidity        roundness       Compactness    
##  Min.   :0.5553   Min.   :0.9192   Min.   :0.4896   Min.   :0.6406  
##  1st Qu.:0.7186   1st Qu.:0.9857   1st Qu.:0.8321   1st Qu.:0.7625  
##  Median :0.7599   Median :0.9883   Median :0.8832   Median :0.8013  
##  Mean   :0.7497   Mean   :0.9871   Mean   :0.8733   Mean   :0.7999  
##  3rd Qu.:0.7869   3rd Qu.:0.9900   3rd Qu.:0.9169   3rd Qu.:0.8343  
##  Max.   :0.8662   Max.   :0.9947   Max.   :0.9907   Max.   :0.9873  
##   ShapeFactor1       ShapeFactor2        ShapeFactor3     ShapeFactor4   
##  Min.   :0.002778   Min.   :0.0005642   Min.   :0.4103   Min.   :0.9477  
##  1st Qu.:0.005900   1st Qu.:0.0011535   1st Qu.:0.5814   1st Qu.:0.9937  
##  Median :0.006645   Median :0.0016935   Median :0.6420   Median :0.9964  
##  Mean   :0.006564   Mean   :0.0017159   Mean   :0.6436   Mean   :0.9951  
##  3rd Qu.:0.007271   3rd Qu.:0.0021703   3rd Qu.:0.6960   3rd Qu.:0.9979  
##  Max.   :0.010451   Max.   :0.0036650   Max.   :0.9748   Max.   :0.9997  
##  ClassBARBUNYA      ClassBOMBAY        ClassCALI      ClassDERMASON   
##  Min.   :0.00000   Min.   :0.00000   Min.   :0.0000   Min.   :0.0000  
##  1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.0000   1st Qu.:0.0000  
##  Median :0.00000   Median :0.00000   Median :0.0000   Median :0.0000  
##  Mean   :0.09713   Mean   :0.03835   Mean   :0.1198   Mean   :0.2605  
##  3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.0000   3rd Qu.:1.0000  
##  Max.   :1.00000   Max.   :1.00000   Max.   :1.0000   Max.   :1.0000  
##    ClassHOROZ       ClassSEKER       ClassSIRA     
##  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
##  1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000  
##  Median :0.0000   Median :0.0000   Median :0.0000  
##  Mean   :0.1417   Mean   :0.1489   Mean   :0.1937  
##  3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.:0.0000  
##  Max.   :1.0000   Max.   :1.0000   Max.   :1.0000
dry_bean_dataset_one_hot_1000_df <- dry_bean_dataset_one_hot_df[sample(nrow(dry_bean_dataset_one_hot_df), size = 1000, replace = FALSE), ]
head(str(dry_bean_dataset_one_hot_1000_df))
## 'data.frame':    1000 obs. of  23 variables:
##  $ Area           : num  95754 43864 22144 27940 53196 ...
##  $ Perimeter      : num  1182 799 558 615 905 ...
##  $ MajorAxisLength: num  453 303 199 227 364 ...
##  $ MinorAxisLength: num  273 184 143 157 187 ...
##  $ AspectRation   : num  1.66 1.65 1.39 1.45 1.95 ...
##  $ Eccentricity   : num  0.799 0.794 0.695 0.723 0.859 ...
##  $ ConvexArea     : num  97441 44336 22445 28256 53781 ...
##  $ EquivDiameter  : num  349 236 168 189 260 ...
##  $ Extent         : num  0.749 0.733 0.72 0.808 0.775 ...
##  $ Solidity       : num  0.983 0.989 0.987 0.989 0.989 ...
##  $ roundness      : num  0.861 0.863 0.895 0.929 0.817 ...
##  $ Compactness    : num  0.771 0.779 0.843 0.83 0.715 ...
##  $ ShapeFactor1   : num  0.00473 0.00692 0.00899 0.00813 0.00685 ...
##  $ ShapeFactor2   : num  0.00103 0.00157 0.0028 0.00238 0.0011 ...
##  $ ShapeFactor3   : num  0.595 0.607 0.711 0.689 0.511 ...
##  $ ShapeFactor4   : num  0.988 0.998 0.989 0.998 0.996 ...
##  $ ClassBARBUNYA  : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ ClassBOMBAY    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ ClassCALI      : num  1 0 0 0 0 1 0 0 0 1 ...
##  $ ClassDERMASON  : num  0 0 1 1 0 0 0 0 1 0 ...
##  $ ClassHOROZ     : num  0 0 0 0 1 0 0 0 0 0 ...
##  $ ClassSEKER     : num  0 0 0 0 0 0 0 1 0 0 ...
##  $ ClassSIRA      : num  0 1 0 0 0 0 1 0 0 0 ...
## NULL
##Persistent Homology of DryBean dataset

# calculate persistent homology for DryBean Dataset
phom_drybean_df <- calculate_homology(dry_bean_dataset_one_hot_1000_df)

# plot barcode for DryBean Dataset
plot_barcode(phom_drybean_df)

# plot persistent diagram of DryBean Dataset
plot_persist(phom_drybean_df)

#####———————————————MAPPER ALGORITHM————————————————

#Prepare Dry Bean dataset for Mapper 1D algorithm

##Two Filter Functions PCA & KDE

#Prepare linear PCA as a filter function by centering and scaling dataset first on all one hot df dataset
b<- prcomp(dry_bean_dataset_one_hot_df, center=TRUE, scale=TRUE)
ts_dry_bean_pca_b <- as.data.frame(predict(b, dry_bean_dataset_one_hot_df))

#Conduct kernel density estimator as a filter function on 4 of 6
filter.kde <- kde(dry_bean_dataset_one_hot_df[,1:4],H=diag(1,nrow = 4),eval.points = dry_bean_dataset_one_hot_df[,1:4])$estimate


##*** dry_bean_dataset Mapper 5 intervals, 40% overlap, 5 bins

m_dry_bean_dataset_5.40.5 <- mapper1D(
     distance_matrix = dist(dry_bean_dataset_one_hot_df),
     filter_values = c(ts_dry_bean_pca_b$PC1),
     num_intervals = 5,
     percent_overlap = 40,
     num_bins_when_clustering = 5)


g_dry_bean_dataset_5.40.5 <- graph.adjacency(m_dry_bean_dataset_5.40.5$adjacency, mode="undirected")
## Warning: `graph.adjacency()` was deprecated in igraph 2.0.0.
## ℹ Please use `graph_from_adjacency_matrix()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
plot(g_dry_bean_dataset_5.40.5, layout = layout.auto(g_dry_bean_dataset_5.40.5))
## Warning: `layout.auto()` was deprecated in igraph 2.0.0.
## ℹ Please use `layout_nicely()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

head(str(m_dry_bean_dataset_5.40.5$level_of_vertex))
##  int [1:5] 1 2 3 4 5
## NULL
head(str(m_dry_bean_dataset_5.40.5$vertices_in_level))
## List of 5
##  $ : num 1
##  $ : num 2
##  $ : num 3
##  $ : num 4
##  $ : num 5
## NULL
head(str(m_dry_bean_dataset_5.40.5$points_in_vertex))
## List of 5
##  $ : int [1:6835] 1 2 3 4 5 6 7 8 9 10 ...
##  $ : int [1:8024] 272 279 431 433 457 646 667 713 759 798 ...
##  $ : int [1:5008] 272 2028 2046 2054 2055 2056 2059 2060 2063 2064 ...
##  $ : int [1:894] 2647 2935 2951 2987 3064 3066 3081 3082 3084 3093 ...
##  $ : int [1:342] 3375 3424 3427 3428 3430 3435 3437 3450 3453 3456 ...
## NULL
my_resolution = 100
my_palette = colorRampPalette(c('red','green','lightblue'))
my_max = max(m_dry_bean_dataset_5.40.5$level_of_vertex, na.rm=TRUE)
my_vector = m_dry_bean_dataset_5.40.5$level_of_vertex / my_max

my_colors = my_palette(my_resolution)[as.numeric(cut(
                       my_vector, breaks=my_resolution))]

g_dry_bean_dataset_5.40.5 <- graph.adjacency(m_dry_bean_dataset_5.40.5$adjacency, mode="undirected")
vertex_size <- unlist(lapply(m_dry_bean_dataset_5.40.5$points_in_vertex,
                             function(x) length(x)))

plot(g_dry_bean_dataset_5.40.5, layout = layout.auto(g_dry_bean_dataset_5.40.5),
     vertex.size = 30*log(vertex_size)/
     max(log(vertex_size)),
     vertex.color = my_colors)

m_dry_bean_dataset_5.40.5.n1<-m_dry_bean_dataset_5.40.5$points_in_vertex[1]
    m_dry_bean_dataset_5.40.5.n1.vec<-as.vector(unlist(m_dry_bean_dataset_5.40.5.n1))
m_dry_bean_dataset_5.40.5.n2<-m_dry_bean_dataset_5.40.5$points_in_vertex[2]
    m_dry_bean_dataset_5.40.5.n2.vec<-as.vector(unlist(m_dry_bean_dataset_5.40.5.n2)) 
m_dry_bean_dataset_5.40.5.n3<-m_dry_bean_dataset_5.40.5$points_in_vertex[3]
    m_dry_bean_dataset_5.40.5.n3.vec<-as.vector(unlist(m_dry_bean_dataset_5.40.5.n3))
m_dry_bean_dataset_5.40.5.n4<-m_dry_bean_dataset_5.40.5$points_in_vertex[4]
    m_dry_bean_dataset_5.40.5.n4.vec<-as.vector(unlist(m_dry_bean_dataset_5.40.5.n4)) 
m_dry_bean_dataset_5.40.5.n5<-m_dry_bean_dataset_5.40.5$points_in_vertex[5]
    m_dry_bean_dataset_5.40.5.n5.vec<-as.vector(unlist(m_dry_bean_dataset_5.40.5.n5))

##map the ID’s of each Mapper vertex point to the actual dry_bean_dataset One Hot DF1 dataset
tda.m_dry_bean_dataset_5.40.5.n1.vec<-Dry_Bean_Dataset[m_dry_bean_dataset_5.40.5.n1.vec,]
tda.m_dry_bean_dataset_5.40.5.n2.vec<-Dry_Bean_Dataset[m_dry_bean_dataset_5.40.5.n2.vec,]
tda.m_dry_bean_dataset_5.40.5.n3.vec<-Dry_Bean_Dataset[m_dry_bean_dataset_5.40.5.n3.vec,]
tda.m_dry_bean_dataset_5.40.5.n4.vec<-Dry_Bean_Dataset[m_dry_bean_dataset_5.40.5.n4.vec,]
tda.m_dry_bean_dataset_5.40.5.n5.vec<-Dry_Bean_Dataset[m_dry_bean_dataset_5.40.5.n5.vec,]



##*** dry_bean_dataset Mapper KDE 5 intervals, 40% overlap, 5 bins

m_kde_dry_bean_dataset_5.40.5 <- mapper1D(
     distance_matrix = dist(dry_bean_dataset_one_hot_df),
     filter_values = c(filter.kde),
     num_intervals = 5,
     percent_overlap = 40,
     num_bins_when_clustering = 5)


g_kde_dry_bean_dataset_5.40.5 <- graph.adjacency(m_kde_dry_bean_dataset_5.40.5$adjacency, mode="undirected")
plot(g_kde_dry_bean_dataset_5.40.5, layout = layout.auto(g_kde_dry_bean_dataset_5.40.5))

head(str(m_kde_dry_bean_dataset_5.40.5$level_of_vertex))
##  int [1:5] 1 2 3 4 5
## NULL
head(str(m_kde_dry_bean_dataset_5.40.5$vertices_in_level))
## List of 5
##  $ : num 1
##  $ : num 2
##  $ : num 3
##  $ : num 4
##  $ : num 5
## NULL
head(str(m_kde_dry_bean_dataset_5.40.5$points_in_vertex))
## List of 5
##  $ : int [1:7503] 1 2 3 4 5 6 7 8 9 10 ...
##  $ : int [1:7002] 1 3 4 6 8 9 10 11 13 14 ...
##  $ : int [1:3511] 25 108 159 183 197 198 202 206 209 211 ...
##  $ : int [1:1759] 294 369 374 376 401 402 409 413 431 433 ...
##  $ : int [1:774] 548 593 615 616 618 631 633 638 640 646 ...
## NULL
my_resolution = 100
my_palette = colorRampPalette(c('red','green','lightblue'))
my_max = max(m_kde_dry_bean_dataset_5.40.5$level_of_vertex, na.rm=TRUE)
my_vector = m_kde_dry_bean_dataset_5.40.5$level_of_vertex / my_max

my_colors = my_palette(my_resolution)[as.numeric(cut(
                       my_vector, breaks=my_resolution))]

g_kde_dry_bean_dataset_5.40.5 <- graph.adjacency(m_kde_dry_bean_dataset_5.40.5$adjacency, mode="undirected")
vertex_size <- unlist(lapply(m_kde_dry_bean_dataset_5.40.5$points_in_vertex,
                             function(x) length(x)))

plot(g_kde_dry_bean_dataset_5.40.5, layout = layout.auto(g_kde_dry_bean_dataset_5.40.5),
     vertex.size = 30*log(vertex_size)/
     max(log(vertex_size)),
     vertex.color = my_colors)

##Extract the ID observations of each mapper output vertex
m_kde_dry_bean_dataset_5.40.5.n1<-m_kde_dry_bean_dataset_5.40.5$points_in_vertex[1]
    m_kde_dry_bean_dataset_5.40.5.n1.vec<-as.vector(unlist(m_kde_dry_bean_dataset_5.40.5.n1))
m_kde_dry_bean_dataset_5.40.5.n2<-m_kde_dry_bean_dataset_5.40.5$points_in_vertex[2]
    m_kde_dry_bean_dataset_5.40.5.n2.vec<-as.vector(unlist(m_kde_dry_bean_dataset_5.40.5.n2)) 
m_kde_dry_bean_dataset_5.40.5.n3<-m_kde_dry_bean_dataset_5.40.5$points_in_vertex[3]
    m_kde_dry_bean_dataset_5.40.5.n3.vec<-as.vector(unlist(m_kde_dry_bean_dataset_5.40.5.n3))
m_kde_dry_bean_dataset_5.40.5.n4<-m_kde_dry_bean_dataset_5.40.5$points_in_vertex[4]
    m_kde_dry_bean_dataset_5.40.5.n4.vec<-as.vector(unlist(m_kde_dry_bean_dataset_5.40.5.n4)) 
m_kde_dry_bean_dataset_5.40.5.n5<-m_kde_dry_bean_dataset_5.40.5 $points_in_vertex[5]
    m_kde_dry_bean_dataset_5.40.5.n5.vec<-as.vector(unlist(m_kde_dry_bean_dataset_5.40.5.n5))

##map the ID’s of each Mapper vertex point to the actual dry_bean_dataset One Hot DF4 dataset
tda.m_kde_dry_bean_dataset_5.40.5.n1.vec<-Dry_Bean_Dataset[m_kde_dry_bean_dataset_5.40.5.n1.vec,]
tda.m_kde_dry_bean_dataset_5.40.5.n2.vec<-Dry_Bean_Dataset[m_kde_dry_bean_dataset_5.40.5.n2.vec,]
tda.m_kde_dry_bean_dataset_5.40.5.n3.vec<-Dry_Bean_Dataset[m_kde_dry_bean_dataset_5.40.5.n3.vec,]
tda.m_kde_dry_bean_dataset_5.40.5.n4.vec<-Dry_Bean_Dataset[m_kde_dry_bean_dataset_5.40.5.n4.vec,]
tda.m_kde_dry_bean_dataset_5.40.5.n5.vec<-Dry_Bean_Dataset[m_kde_dry_bean_dataset_5.40.5.n5.vec,]
library(caret)

trainIndex <- createDataPartition(Dry_Bean_Dataset$Class, p = .7, 
                                  list = FALSE, 
                                  times = 1)

head(trainIndex)
##      Resample1
## [1,]         1
## [2,]         3
## [3,]         4
## [4,]         5
## [5,]         7
## [6,]         8
Dry_Bean_DatasetTrain <- Dry_Bean_Dataset[ trainIndex,]
Dry_Bean_DatasetTest  <- Dry_Bean_Dataset[-trainIndex,]
#Train Control: k-Fold Cross-validation basis for all models 
fitControl <- trainControl(## 10-fold CV
                           method = "cv",
                           number = 3)
#Non-TDA-Assited
rfGrid<-expand.grid(mtry = (1:20)*50)
#Random Forest 
dryBeanRfFit <- train(as.factor(Class) ~ ., data = Dry_Bean_DatasetTrain, 
                 Importance = T,
                 method = 'rf', 
                 trControl = fitControl,
                 tuneGrid = rfGrid, preProc = c('center','scale'),                    
                 metric='Accuracy')
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
dryBeanRfFit
## Random Forest 
## 
## 9531 samples
##   16 predictor
##    7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## Pre-processing: centered (16), scaled (16) 
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 6354, 6354, 6354 
## Resampling results across tuning parameters:
## 
##   mtry  Accuracy   Kappa    
##     50  0.9199454  0.9031325
##    100  0.9195258  0.9026320
##    150  0.9198405  0.9030070
##    200  0.9192110  0.9022480
##    250  0.9191061  0.9021262
##    300  0.9192110  0.9022501
##    350  0.9193159  0.9023746
##    400  0.9191061  0.9021176
##    450  0.9196307  0.9027497
##    500  0.9196307  0.9027488
##    550  0.9194208  0.9024972
##    600  0.9186864  0.9016126
##    650  0.9191061  0.9021327
##    700  0.9193159  0.9023720
##    750  0.9204700  0.9037695
##    800  0.9203651  0.9036391
##    850  0.9192110  0.9022435
##    900  0.9195258  0.9026241
##    950  0.9196307  0.9027567
##   1000  0.9188962  0.9018679
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 750.
dryBeanRfFit$resample
##    Accuracy     Kappa Resample
## 1 0.9222537 0.9059559    Fold1
## 2 0.9257161 0.9101383    Fold3
## 3 0.9134404 0.8952144    Fold2
db_rf_fit_re<-dryBeanRfFit$resample[1]


summary(dryBeanRfFit)
##                 Length Class      Mode     
## call                5  -none-     call     
## type                1  -none-     character
## predicted        9531  factor     numeric  
## err.rate         4000  -none-     numeric  
## confusion          56  -none-     numeric  
## votes           66717  matrix     numeric  
## oob.times        9531  -none-     numeric  
## classes             7  -none-     character
## importance         16  -none-     numeric  
## importanceSD        0  -none-     NULL     
## localImportance     0  -none-     NULL     
## proximity           0  -none-     NULL     
## ntree               1  -none-     numeric  
## mtry                1  -none-     numeric  
## forest             14  -none-     list     
## y                9531  factor     numeric  
## test                0  -none-     NULL     
## inbag               0  -none-     NULL     
## xNames             16  -none-     character
## problemType         1  -none-     character
## tuneValue           1  data.frame list     
## obsLevels           7  -none-     character
## param               1  -none-     list
vip(dryBeanRfFit,25) + ggtitle("non-TDA-Assisted: RF")

# Predict outcome using model from training data based on testing data
predictions <- predict(dryBeanRfFit, newdata = Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
db_rf_cf<-confusionMatrix(data=predictions, as.factor(Dry_Bean_DatasetTest$Class))
db_rf_cf
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      344      0   15        0     2     5    3
##   BOMBAY          1    156    0        0     0     0    0
##   CALI           28      0  456        0    12     0    1
##   DERMASON        0      0    0      997     5    17   69
##   HOROZ           6      0   12        2   552     0   14
##   SEKER           5      0    2       11     0   575   13
##   SIRA           12      0    4       53     7    11  690
## 
## Overall Statistics
##                                          
##                Accuracy : 0.924          
##                  95% CI : (0.9155, 0.932)
##     No Information Rate : 0.2605         
##     P-Value [Acc > NIR] : < 2.2e-16      
##                                          
##                   Kappa : 0.908          
##                                          
##  Mcnemar's Test P-Value : NA             
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.86869       1.00000      0.9325          0.9379
## Specificity                  0.99321       0.99975      0.9886          0.9698
## Pos Pred Value               0.93225       0.99363      0.9175          0.9164
## Neg Pred Value               0.98599       1.00000      0.9908          0.9779
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.08431       0.03824      0.1118          0.2444
## Detection Prevalence         0.09044       0.03848      0.1218          0.2667
## Balanced Accuracy            0.93095       0.99987      0.9605          0.9539
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9550       0.9457      0.8734
## Specificity                0.9903       0.9911      0.9736
## Pos Pred Value             0.9420       0.9488      0.8880
## Neg Pred Value             0.9926       0.9905      0.9697
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1353       0.1409      0.1691
## Detection Prevalence       0.1436       0.1485      0.1904
## Balanced Accuracy          0.9727       0.9684      0.9235
db_rf_cf$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.9240196      0.9080495      0.9154592      0.9319667      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
db_rf_cf_ov_acc<-db_rf_cf$overall[1]
db_rf_cf$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.8686869   0.9932139      0.9322493      0.9859876 0.9322493
## Class: BOMBAY     1.0000000   0.9997452      0.9936306      1.0000000 0.9936306
## Class: CALI       0.9325153   0.9885826      0.9175050      0.9907898 0.9175050
## Class: DERMASON   0.9379116   0.9698376      0.9163603      0.9779412 0.9163603
## Class: HOROZ      0.9550173   0.9902913      0.9419795      0.9925587 0.9419795
## Class: SEKER      0.9457237   0.9910714      0.9488449      0.9905009 0.9488449
## Class: SIRA       0.8734177   0.9735562      0.8880309      0.9697245 0.8880309
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.8686869 0.8993464 0.09705882     0.08431373
## Class: BOMBAY   1.0000000 0.9968051 0.03823529     0.03823529
## Class: CALI     0.9325153 0.9249493 0.11985294     0.11176471
## Class: DERMASON 0.9379116 0.9270107 0.26053922     0.24436275
## Class: HOROZ    0.9550173 0.9484536 0.14166667     0.13529412
## Class: SEKER    0.9457237 0.9472817 0.14901961     0.14093137
## Class: SIRA     0.8734177 0.8806637 0.19362745     0.16911765
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA           0.09044118         0.9309504
## Class: BOMBAY             0.03848039         0.9998726
## Class: CALI               0.12181373         0.9605490
## Class: DERMASON           0.26666667         0.9538746
## Class: HOROZ              0.14362745         0.9726543
## Class: SEKER              0.14852941         0.9683976
## Class: SIRA               0.19044118         0.9234870
db_rf_cf_pre_rec_f1<-db_rf_cf$byClass[5:7]


##With TDA PCA filter 5 intervals, 50% overlap, 5 bins 
##Node1

DryBean_TDA_PC_5.40.5_n1_RfFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.40.5.n1.vec, 
                 Importance = T,
                 method = 'rf', 
                 trControl = fitControl,
                 tuneGrid = rfGrid, preProc = c('center','scale'), 
                 metric='Accuracy')
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
DryBean_TDA_PC_5.40.5_n1_RfFit0
## Random Forest 
## 
## 6835 samples
##   16 predictor
##    6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## Pre-processing: centered (16), scaled (16) 
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 4556, 4557, 4557 
## Resampling results across tuning parameters:
## 
##   mtry  Accuracy   Kappa    
##     50  0.9073882  0.8474409
##    100  0.9070956  0.8471241
##    150  0.9078270  0.8482150
##    200  0.9066566  0.8462709
##    250  0.9073888  0.8476041
##    300  0.9069493  0.8468482
##    350  0.9076809  0.8480734
##    400  0.9066569  0.8463018
##    450  0.9057786  0.8449853
##    500  0.9076810  0.8481543
##    550  0.9066567  0.8463483
##    600  0.9072421  0.8472366
##    650  0.9060714  0.8453521
##    700  0.9060716  0.8454251
##    750  0.9072419  0.8473494
##    800  0.9075346  0.8478233
##    850  0.9072419  0.8471324
##    900  0.9066569  0.8463292
##    950  0.9065102  0.8460345
##   1000  0.9075347  0.8477997
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 150.
DryBean_TDA_PC_5.40.5_n1_RfFit0$resample
##    Accuracy     Kappa Resample
## 1 0.9086918 0.8490493    Fold2
## 2 0.9104871 0.8531404    Fold1
## 3 0.9043020 0.8424554    Fold3
db_tda_pc_5.40.5_n1_rf_fit0_re<-DryBean_TDA_PC_5.40.5_n1_RfFit0$resample[1]


summary(DryBean_TDA_PC_5.40.5_n1_RfFit0)
##                 Length Class      Mode     
## call                5  -none-     call     
## type                1  -none-     character
## predicted        6835  factor     numeric  
## err.rate         3500  -none-     numeric  
## confusion          42  -none-     numeric  
## votes           41010  matrix     numeric  
## oob.times        6835  -none-     numeric  
## classes             6  -none-     character
## importance         16  -none-     numeric  
## importanceSD        0  -none-     NULL     
## localImportance     0  -none-     NULL     
## proximity           0  -none-     NULL     
## ntree               1  -none-     numeric  
## mtry                1  -none-     numeric  
## forest             14  -none-     list     
## y                6835  factor     numeric  
## test                0  -none-     NULL     
## inbag               0  -none-     NULL     
## xNames             16  -none-     character
## problemType         1  -none-     character
## tuneValue           1  data.frame list     
## obsLevels           6  -none-     character
## param               1  -none-     list
vip(DryBean_TDA_PC_5.40.5_n1_RfFit0,25) + ggtitle("Adult_TDA_PCA_5.40.5_n1_RfFit TDA-Assited RF")

# Predict outcome using DryBean_TDA_PC_5.40.5_n1_RfFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.40.5_n1_RfFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.40.5_n1_rf_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in confusionMatrix.default(data = pred0,
## as.factor(Dry_Bean_DatasetTest$Class)): Levels are not in the same order for
## reference and data. Refactoring data to match.
db_tda_pc_5.40.5_n1_rf_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA       12      1    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    1        0     0     0    0
##   DERMASON        0      0    0     1062   226     1   30
##   HOROZ           0      0    0        0     1     0    0
##   SEKER         363    151  486        0   269   607  210
##   SIRA           21      4    2        1    82     0  550
## 
## Overall Statistics
##                                           
##                Accuracy : 0.5473          
##                  95% CI : (0.5319, 0.5627)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.4397          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                 0.030303       0.00000   0.0020450          0.9991
## Specificity                 0.999729       1.00000   1.0000000          0.9148
## Pos Pred Value              0.923077           NaN   1.0000000          0.8052
## Neg Pred Value              0.905582       0.96176   0.8803628          0.9996
## Prevalence                  0.097059       0.03824   0.1198529          0.2605
## Detection Rate              0.002941       0.00000   0.0002451          0.2603
## Detection Prevalence        0.003186       0.00000   0.0002451          0.3233
## Balanced Accuracy           0.515016       0.50000   0.5010225          0.9569
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity             0.0017301       0.9984      0.6962
## Specificity             1.0000000       0.5740      0.9666
## Pos Pred Value          1.0000000       0.2910      0.8333
## Neg Pred Value          0.8585438       0.9995      0.9298
## Prevalence              0.1416667       0.1490      0.1936
## Detection Rate          0.0002451       0.1488      0.1348
## Detection Prevalence    0.0002451       0.5113      0.1618
## Balanced Accuracy       0.5008651       0.7862      0.8314
db_tda_pc_5.40.5_n1_rf_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA       12      1    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    1        0     0     0    0
##   DERMASON        0      0    0     1062   226     1   30
##   HOROZ           0      0    0        0     1     0    0
##   SEKER         363    151  486        0   269   607  210
##   SIRA           21      4    2        1    82     0  550
## 
## Overall Statistics
##                                           
##                Accuracy : 0.5473          
##                  95% CI : (0.5319, 0.5627)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.4397          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                 0.030303       0.00000   0.0020450          0.9991
## Specificity                 0.999729       1.00000   1.0000000          0.9148
## Pos Pred Value              0.923077           NaN   1.0000000          0.8052
## Neg Pred Value              0.905582       0.96176   0.8803628          0.9996
## Prevalence                  0.097059       0.03824   0.1198529          0.2605
## Detection Rate              0.002941       0.00000   0.0002451          0.2603
## Detection Prevalence        0.003186       0.00000   0.0002451          0.3233
## Balanced Accuracy           0.515016       0.50000   0.5010225          0.9569
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity             0.0017301       0.9984      0.6962
## Specificity             1.0000000       0.5740      0.9666
## Pos Pred Value          1.0000000       0.2910      0.8333
## Neg Pred Value          0.8585438       0.9995      0.9298
## Prevalence              0.1416667       0.1490      0.1936
## Detection Rate          0.0002451       0.1488      0.1348
## Detection Prevalence    0.0002451       0.5113      0.1618
## Balanced Accuracy       0.5008651       0.7862      0.8314
db_tda_pc_5.40.5_n1_rf_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.5473039      0.4396538      0.5318787      0.5626614      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
db_tda_pc_5.40.5_n1_rf_cf0_ov_acc<-db_tda_pc_5.40.5_n1_rf_cf0$overall[1]
db_tda_pc_5.40.5_n1_rf_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.030303030   0.9997286      0.9230769      0.9055815 0.9230769
## Class: BOMBAY   0.000000000   1.0000000            NaN      0.9617647        NA
## Class: CALI     0.002044990   1.0000000      1.0000000      0.8803628 1.0000000
## Class: DERMASON 0.999059266   0.9148160      0.8051554      0.9996378 0.8051554
## Class: HOROZ    0.001730104   1.0000000      1.0000000      0.8585438 1.0000000
## Class: SEKER    0.998355263   0.5740207      0.2909875      0.9994985 0.2909875
## Class: SIRA     0.696202532   0.9665653      0.8333333      0.9298246 0.8333333
##                      Recall          F1 Prevalence Detection Rate
## Class: BARBUNYA 0.030303030 0.058679707 0.09705882    0.002941176
## Class: BOMBAY   0.000000000          NA 0.03823529    0.000000000
## Class: CALI     0.002044990 0.004081633 0.11985294    0.000245098
## Class: DERMASON 0.999059266 0.891687657 0.26053922    0.260294118
## Class: HOROZ    0.001730104 0.003454231 0.14166667    0.000245098
## Class: SEKER    0.998355263 0.450631032 0.14901961    0.148774510
## Class: SIRA     0.696202532 0.758620690 0.19362745    0.134803922
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA          0.003186275         0.5150158
## Class: BOMBAY            0.000000000         0.5000000
## Class: CALI              0.000245098         0.5010225
## Class: DERMASON          0.323284314         0.9569377
## Class: HOROZ             0.000245098         0.5008651
## Class: SEKER             0.511274510         0.7861880
## Class: SIRA              0.161764706         0.8313839
db_tda_pc_5.40.5_n1_rf_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n1_rf_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_pca_5.40.5_rf_n1_3_fold<-(db_rf_fit_re-db_tda_pc_5.40.5_n1_rf_fit0_re)
diff_drybean_tda_pca_5.40.5_rf_n1_3_fold
##      Accuracy
## 1 0.013561864
## 2 0.015229029
## 3 0.009138333
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_rf.n1_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_rf_n1_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_rf.n1_3_fold
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_rf.n1_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_rf.n1_3_fold$probLeft/bst_dbf_db_tda_pca_5.40.5_rf.n1_3_fold$probRight
bst_dbf_db_tda_pca_5.40.5_rf.n1_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_rf.n1_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_rf_n1_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_rf.n1_3_fold
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.3092
## 
## $winRight
## [1] 0.6908
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_rf.n1_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_rf_n1_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_rf.n1_3_fold
## $left
## [1] 0.004239549
## 
## $rope
## [1] 0.1632107
## 
## $right
## [1] 0.8325498
# Rope Plot
plot(rope(as.matrix(diff_drybean_tda_pca_5.40.5_rf_n1_3_fold),c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_rf.n1_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_rf_n1_3_fold))
#bf_tda_pca_5.40.5_rf.n1_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.40.5_rf_n1_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_pca_5.40.5_rf_n1_3_fold)
## t = 6.9572, df = 2, p-value = 0.02004
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  0.004824048 0.020462102
## sample estimates:
##  mean of x 
## 0.01264308
### Test set diff
diff_drybean_tda_pca_5.40.5_rf.n1_test<-(db_rf_cf_ov_acc-db_tda_pc_5.40.5_n1_rf_cf0_ov_acc)
diff_drybean_tda_pca_5.40.5_rf.n1_test
##  Accuracy 
## 0.3767157
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_rf.n1_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_rf.n1_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_rf.n1_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_rf.n1_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_rf.n1_test$probLeft/bst_dbf_db_tda_pca_5.40.5_rf.n1_test$probRight
bst_dbf_db_tda_pca_5.40.5_rf.n1_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_rf.n1_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_rf.n1_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_rf.n1_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1589
## 
## $winRight
## [1] 0.8411
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_rf.n1_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_rf.n1_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_rf.n1_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_rf.n1_test)))

#BayesFactor
#bf_tda_pca_5.40.5_rf.n1_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_rf.n1_test)) #bf_tda_pca_5.40.5_rf.n1_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_rf.n1_test))

##Node2

DryBean_TDA_PC_5.40.5_n2_RfFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.40.5.n2.vec, 
                 Importance = T,
                 method = 'rf', 
                 trControl = fitControl,
                 tuneGrid = rfGrid, preProc = c('center','scale'), 
                 metric='Accuracy')
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
DryBean_TDA_PC_5.40.5_n2_RfFit0
## Random Forest 
## 
## 8024 samples
##   16 predictor
##    6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## Pre-processing: centered (16), scaled (16) 
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 5349, 5350, 5349 
## Resampling results across tuning parameters:
## 
##   mtry  Accuracy   Kappa    
##     50  0.8849708  0.8501869
##    100  0.8867155  0.8524533
##    150  0.8845966  0.8496950
##    200  0.8857183  0.8511796
##    250  0.8862168  0.8518103
##    300  0.8855937  0.8510037
##    350  0.8850952  0.8503930
##    400  0.8863416  0.8519450
##    450  0.8864661  0.8521268
##    500  0.8853446  0.8506606
##    550  0.8840984  0.8490371
##    600  0.8860923  0.8516366
##    650  0.8863414  0.8519889
##    700  0.8864664  0.8521416
##    750  0.8838489  0.8487549
##    800  0.8854693  0.8508190
##    850  0.8849707  0.8501588
##    900  0.8862169  0.8517975
##    950  0.8872141  0.8531025
##   1000  0.8858430  0.8513187
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 950.
DryBean_TDA_PC_5.40.5_n2_RfFit0$resample
##    Accuracy     Kappa Resample
## 1 0.8829907 0.8475219    Fold1
## 2 0.8852336 0.8506156    Fold3
## 3 0.8934181 0.8611702    Fold2
db_tda_pc_5.40.5_n2_rf_fit0_re<-DryBean_TDA_PC_5.40.5_n2_RfFit0$resample[1]


summary(DryBean_TDA_PC_5.40.5_n2_RfFit0)
##                 Length Class      Mode     
## call                5  -none-     call     
## type                1  -none-     character
## predicted        8024  factor     numeric  
## err.rate         3500  -none-     numeric  
## confusion          42  -none-     numeric  
## votes           48144  matrix     numeric  
## oob.times        8024  -none-     numeric  
## classes             6  -none-     character
## importance         16  -none-     numeric  
## importanceSD        0  -none-     NULL     
## localImportance     0  -none-     NULL     
## proximity           0  -none-     NULL     
## ntree               1  -none-     numeric  
## mtry                1  -none-     numeric  
## forest             14  -none-     list     
## y                8024  factor     numeric  
## test                0  -none-     NULL     
## inbag               0  -none-     NULL     
## xNames             16  -none-     character
## problemType         1  -none-     character
## tuneValue           1  data.frame list     
## obsLevels           6  -none-     character
## param               1  -none-     list
vip(DryBean_TDA_PC_5.40.5_n2_RfFit0,25) + ggtitle("Adult_TDA_PCA_5.40.5_n2_RfFit TDA-Assited RF")

# Predict outcome using DryBean_TDA_PC_5.40.5_n2_RfFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.40.5_n2_RfFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.40.5_n2_rf_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in confusionMatrix.default(data = pred0,
## as.factor(Dry_Bean_DatasetTest$Class)): Levels are not in the same order for
## reference and data. Refactoring data to match.
db_tda_pc_5.40.5_n2_rf_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      390    114   78        0     2     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            5     42  407        0    18     0    0
##   DERMASON        0      0    0     1014     0   105    0
##   HOROZ           1      0    4        0   471     0    0
##   SEKER           0      0    0        0     0   277    0
##   SIRA            0      0    0       49    87   226  790
## 
## Overall Statistics
##                                           
##                Accuracy : 0.8208          
##                  95% CI : (0.8087, 0.8325)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.7814          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.98485       0.00000     0.83231          0.9539
## Specificity                  0.94734       1.00000     0.98190          0.9652
## Pos Pred Value               0.66781           NaN     0.86229          0.9062
## Neg Pred Value               0.99828       0.96176     0.97727          0.9835
## Prevalence                   0.09706       0.03824     0.11985          0.2605
## Detection Rate               0.09559       0.00000     0.09975          0.2485
## Detection Prevalence         0.14314       0.00000     0.11569          0.2743
## Balanced Accuracy            0.96609       0.50000     0.90711          0.9596
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.8149      0.45559      1.0000
## Specificity                0.9986      1.00000      0.8900
## Pos Pred Value             0.9895      1.00000      0.6858
## Neg Pred Value             0.9703      0.91296      1.0000
## Prevalence                 0.1417      0.14902      0.1936
## Detection Rate             0.1154      0.06789      0.1936
## Detection Prevalence       0.1167      0.06789      0.2824
## Balanced Accuracy          0.9067      0.72780      0.9450
db_tda_pc_5.40.5_n2_rf_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      390    114   78        0     2     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            5     42  407        0    18     0    0
##   DERMASON        0      0    0     1014     0   105    0
##   HOROZ           1      0    4        0   471     0    0
##   SEKER           0      0    0        0     0   277    0
##   SIRA            0      0    0       49    87   226  790
## 
## Overall Statistics
##                                           
##                Accuracy : 0.8208          
##                  95% CI : (0.8087, 0.8325)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.7814          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.98485       0.00000     0.83231          0.9539
## Specificity                  0.94734       1.00000     0.98190          0.9652
## Pos Pred Value               0.66781           NaN     0.86229          0.9062
## Neg Pred Value               0.99828       0.96176     0.97727          0.9835
## Prevalence                   0.09706       0.03824     0.11985          0.2605
## Detection Rate               0.09559       0.00000     0.09975          0.2485
## Detection Prevalence         0.14314       0.00000     0.11569          0.2743
## Balanced Accuracy            0.96609       0.50000     0.90711          0.9596
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.8149      0.45559      1.0000
## Specificity                0.9986      1.00000      0.8900
## Pos Pred Value             0.9895      1.00000      0.6858
## Neg Pred Value             0.9703      0.91296      1.0000
## Prevalence                 0.1417      0.14902      0.1936
## Detection Rate             0.1154      0.06789      0.1936
## Detection Prevalence       0.1167      0.06789      0.2824
## Balanced Accuracy          0.9067      0.72780      0.9450
db_tda_pc_5.40.5_n2_rf_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.8208333      0.7813625      0.8087152      0.8324902      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
db_tda_pc_5.40.5_n2_rf_cf0_ov_acc<-db_tda_pc_5.40.5_n2_rf_cf0$overall[1]
db_tda_pc_5.40.5_n2_rf_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.9848485   0.9473398      0.6678082      0.9982838 0.6678082
## Class: BOMBAY     0.0000000   1.0000000            NaN      0.9617647        NA
## Class: CALI       0.8323108   0.9818992      0.8622881      0.9772727 0.8622881
## Class: DERMASON   0.9539040   0.9651972      0.9061662      0.9834515 0.9061662
## Class: HOROZ      0.8148789   0.9985722      0.9894958      0.9703108 0.9894958
## Class: SEKER      0.4555921   1.0000000      1.0000000      0.9129634 1.0000000
## Class: SIRA       1.0000000   0.8899696      0.6857639      1.0000000 0.6857639
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.9848485 0.7959184 0.09705882     0.09558824
## Class: BOMBAY   0.0000000        NA 0.03823529     0.00000000
## Class: CALI     0.8323108 0.8470343 0.11985294     0.09975490
## Class: DERMASON 0.9539040 0.9294225 0.26053922     0.24852941
## Class: HOROZ    0.8148789 0.8937381 0.14166667     0.11544118
## Class: SEKER    0.4555921 0.6259887 0.14901961     0.06789216
## Class: SIRA     1.0000000 0.8135942 0.19362745     0.19362745
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA           0.14313725         0.9660942
## Class: BOMBAY             0.00000000         0.5000000
## Class: CALI               0.11568627         0.9071050
## Class: DERMASON           0.27426471         0.9595506
## Class: HOROZ              0.11666667         0.9067256
## Class: SEKER              0.06789216         0.7277961
## Class: SIRA               0.28235294         0.9449848
db_tda_pc_5.40.5_n2_rf_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n2_rf_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_pca_5.40.5_rf_n2_3_fold<-(db_rf_fit_re-db_tda_pc_5.40.5_n2_rf_fit0_re)
diff_drybean_tda_pca_5.40.5_rf_n2_3_fold
##     Accuracy
## 1 0.03926304
## 2 0.04048244
## 3 0.02002225
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_rf.n2_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_rf_n2_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_rf.n2_3_fold
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_rf.n2_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_rf.n2_3_fold$probLeft/bst_dbf_db_tda_pca_5.40.5_rf.n2_3_fold$probRight
bst_dbf_db_tda_pca_5.40.5_rf.n2_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test

#bsr_dbf_db_tda_pca_5.40.5_rf.n2_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_rf_n2_3_fold),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.40.5_rf.n2_3_fold

# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_rf.n2_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_rf_n2_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_rf.n2_3_fold
## $left
## [1] 0.01494601
## 
## $rope
## [1] 0.03172187
## 
## $right
## [1] 0.9533321
# Rope Plot
#plot(rope(as.matrix(diff_drybean_tda_pca_5.40.5_rf_n2_3_fold),c(-0.01,0.01)))


#BayesFactor
#bf_tda_pca_5.40.5_rf.n2_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_rf_n2_3_fold))
#bf_tda_pca_5.40.5_rf.n2_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.40.5_rf_n2_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_pca_5.40.5_rf_n2_3_fold)
## t = 5.0189, df = 2, p-value = 0.03748
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  0.004745733 0.061766092
## sample estimates:
##  mean of x 
## 0.03325591
### Test set diff
diff_drybean_tda_pca_5.40.5_rf.n2_test<-(db_rf_cf_ov_acc-db_tda_pc_5.40.5_n2_rf_cf0_ov_acc)
diff_drybean_tda_pca_5.40.5_rf.n2_test
##  Accuracy 
## 0.1031863
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_rf.n2_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_rf.n2_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_rf.n2_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_rf.n2_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_rf.n2_test$probLeft/bst_dbf_db_tda_pca_5.40.5_rf.n2_test$probRight
bst_dbf_db_tda_pca_5.40.5_rf.n2_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_rf.n2_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_rf.n2_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_rf.n2_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1557667
## 
## $winRight
## [1] 0.8442333
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_rf.n2_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_rf.n2_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_rf.n2_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(as.matrix(diff_drybean_tda_pca_5.40.5_rf.n2_test),c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_rf.n2_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_rf.n2_test)) #bf_tda_pca_5.40.5_rf.n2_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_rf.n2_test))

##Node3

DryBean_TDA_PC_5.40.5_n3_RfFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.40.5.n3.vec, 
                 Importance = T,
                 method = 'rf', 
                 trControl = fitControl,
                 tuneGrid = rfGrid, preProc = c('center','scale'), 
                 metric='Accuracy')
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry=  50 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 100 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 150 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 200 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 250 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 300 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 350 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 400 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 450 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 500 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 550 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 600 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 650 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 700 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 750 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 800 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 850 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 900 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 950 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry=1000 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
DryBean_TDA_PC_5.40.5_n3_RfFit0
## Random Forest 
## 
## 5008 samples
##   16 predictor
##    7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## Pre-processing: centered (16), scaled (16) 
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 3339, 3338, 3339 
## Resampling results across tuning parameters:
## 
##   mtry  Accuracy   Kappa    
##     50  0.9367885  0.9082584
##    100  0.9361893  0.9074201
##    150  0.9370881  0.9087076
##    200  0.9373877  0.9091516
##    250  0.9355902  0.9065185
##    300  0.9364889  0.9078622
##    350  0.9370881  0.9087224
##    400  0.9373877  0.9091014
##    450  0.9367885  0.9082479
##    500  0.9376872  0.9095814
##    550  0.9364889  0.9078450
##    600  0.9376872  0.9095792
##    650  0.9373877  0.9091605
##    700  0.9367885  0.9082824
##    750  0.9367885  0.9082737
##    800  0.9352906  0.9061078
##    850  0.9364889  0.9078127
##    900  0.9364889  0.9078751
##    950  0.9355902  0.9065339
##   1000  0.9358898  0.9069825
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 500.
DryBean_TDA_PC_5.40.5_n3_RfFit0$resample
##    Accuracy     Kappa Resample
## 1 0.9370881 0.9085753    Fold1
## 2 0.9382864 0.9105876    Fold3
## 3        NA        NA    Fold2
db_tda_pc_5.40.5_n3_rf_fit0_re<-DryBean_TDA_PC_5.40.5_n3_RfFit0$resample[1]


summary(DryBean_TDA_PC_5.40.5_n3_RfFit0)
##                 Length Class      Mode     
## call                5  -none-     call     
## type                1  -none-     character
## predicted        5008  factor     numeric  
## err.rate         4000  -none-     numeric  
## confusion          56  -none-     numeric  
## votes           35056  matrix     numeric  
## oob.times        5008  -none-     numeric  
## classes             7  -none-     character
## importance         16  -none-     numeric  
## importanceSD        0  -none-     NULL     
## localImportance     0  -none-     NULL     
## proximity           0  -none-     NULL     
## ntree               1  -none-     numeric  
## mtry                1  -none-     numeric  
## forest             14  -none-     list     
## y                5008  factor     numeric  
## test                0  -none-     NULL     
## inbag               0  -none-     NULL     
## xNames             16  -none-     character
## problemType         1  -none-     character
## tuneValue           1  data.frame list     
## obsLevels           7  -none-     character
## param               1  -none-     list
vip(DryBean_TDA_PC_5.40.5_n3_RfFit0,25) + ggtitle("Adult_TDA_PCA_5.40.5_n3_RfFit TDA-Assited RF")

# Predict outcome using DryBean_TDA_PC_5.40.5_n3_RfFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.40.5_n3_RfFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.40.5_n3_rf_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
db_tda_pc_5.40.5_n3_rf_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      389      3    0        9     0    29   22
##   BOMBAY          0     90    0        0     0     0    0
##   CALI            1     63  487        0     0   154    4
##   DERMASON        0      0    0        2     0     0    0
##   HOROZ           2      0    2     1048   578   421  527
##   SEKER           0      0    0        0     0     1    0
##   SIRA            4      0    0        4     0     3  237
## 
## Overall Statistics
##                                          
##                Accuracy : 0.4373         
##                  95% CI : (0.422, 0.4526)
##     No Information Rate : 0.2605         
##     P-Value [Acc > NIR] : < 2.2e-16      
##                                          
##                   Kappa : 0.3503         
##                                          
##  Mcnemar's Test P-Value : NA             
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.98232       0.57692      0.9959       0.0018815
## Specificity                  0.98290       1.00000      0.9382       1.0000000
## Pos Pred Value               0.86062       1.00000      0.6869       1.0000000
## Neg Pred Value               0.99807       0.98346      0.9994       0.7398234
## Prevalence                   0.09706       0.03824      0.1199       0.2605392
## Detection Rate               0.09534       0.02206      0.1194       0.0004902
## Detection Prevalence         0.11078       0.02206      0.1738       0.0004902
## Balanced Accuracy            0.98261       0.78846      0.9670       0.5009407
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                1.0000    0.0016447     0.30000
## Specificity                0.4289    1.0000000     0.99666
## Pos Pred Value             0.2242    1.0000000     0.95565
## Neg Pred Value             1.0000    0.8511890     0.85569
## Prevalence                 0.1417    0.1490196     0.19363
## Detection Rate             0.1417    0.0002451     0.05809
## Detection Prevalence       0.6319    0.0002451     0.06078
## Balanced Accuracy          0.7144    0.5008224     0.64833
db_tda_pc_5.40.5_n3_rf_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      389      3    0        9     0    29   22
##   BOMBAY          0     90    0        0     0     0    0
##   CALI            1     63  487        0     0   154    4
##   DERMASON        0      0    0        2     0     0    0
##   HOROZ           2      0    2     1048   578   421  527
##   SEKER           0      0    0        0     0     1    0
##   SIRA            4      0    0        4     0     3  237
## 
## Overall Statistics
##                                          
##                Accuracy : 0.4373         
##                  95% CI : (0.422, 0.4526)
##     No Information Rate : 0.2605         
##     P-Value [Acc > NIR] : < 2.2e-16      
##                                          
##                   Kappa : 0.3503         
##                                          
##  Mcnemar's Test P-Value : NA             
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.98232       0.57692      0.9959       0.0018815
## Specificity                  0.98290       1.00000      0.9382       1.0000000
## Pos Pred Value               0.86062       1.00000      0.6869       1.0000000
## Neg Pred Value               0.99807       0.98346      0.9994       0.7398234
## Prevalence                   0.09706       0.03824      0.1199       0.2605392
## Detection Rate               0.09534       0.02206      0.1194       0.0004902
## Detection Prevalence         0.11078       0.02206      0.1738       0.0004902
## Balanced Accuracy            0.98261       0.78846      0.9670       0.5009407
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                1.0000    0.0016447     0.30000
## Specificity                0.4289    1.0000000     0.99666
## Pos Pred Value             0.2242    1.0000000     0.95565
## Neg Pred Value             1.0000    0.8511890     0.85569
## Prevalence                 0.1417    0.1490196     0.19363
## Detection Rate             0.1417    0.0002451     0.05809
## Detection Prevalence       0.6319    0.0002451     0.06078
## Balanced Accuracy          0.7144    0.5008224     0.64833
db_tda_pc_5.40.5_n3_rf_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   4.372549e-01   3.502756e-01   4.219610e-01   4.526388e-01   2.605392e-01 
## AccuracyPValue  McnemarPValue 
##  3.328936e-131            NaN
db_tda_pc_5.40.5_n3_rf_cf0_ov_acc<-db_tda_pc_5.40.5_n3_rf_cf0$overall[1]
db_tda_pc_5.40.5_n3_rf_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.982323232   0.9828990      0.8606195      0.9980706 0.8606195
## Class: BOMBAY   0.576923077   1.0000000      1.0000000      0.9834586 1.0000000
## Class: CALI     0.995910020   0.9381788      0.6868829      0.9994067 0.6868829
## Class: DERMASON 0.001881468   1.0000000      1.0000000      0.7398234 1.0000000
## Class: HOROZ    1.000000000   0.4288978      0.2242048      1.0000000 0.2242048
## Class: SEKER    0.001644737   1.0000000      1.0000000      0.8511890 1.0000000
## Class: SIRA     0.300000000   0.9966565      0.9556452      0.8556889 0.9556452
##                      Recall          F1 Prevalence Detection Rate
## Class: BARBUNYA 0.982323232 0.917452830 0.09705882   0.0953431373
## Class: BOMBAY   0.576923077 0.731707317 0.03823529   0.0220588235
## Class: CALI     0.995910020 0.813021703 0.11985294   0.1193627451
## Class: DERMASON 0.001881468 0.003755869 0.26053922   0.0004901961
## Class: HOROZ    1.000000000 0.366286439 0.14166667   0.1416666667
## Class: SEKER    0.001644737 0.003284072 0.14901961   0.0002450980
## Class: SIRA     0.300000000 0.456647399 0.19362745   0.0580882353
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA         0.1107843137         0.9826111
## Class: BOMBAY           0.0220588235         0.7884615
## Class: CALI             0.1737745098         0.9670444
## Class: DERMASON         0.0004901961         0.5009407
## Class: HOROZ            0.6318627451         0.7144489
## Class: SEKER            0.0002450980         0.5008224
## Class: SIRA             0.0607843137         0.6483283
db_tda_pc_5.40.5_n3_rf_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n3_rf_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_pca_5.40.5_rf_n3_3_fold<-(db_rf_fit_re-db_tda_pc_5.40.5_n3_rf_fit0_re)
diff_drybean_tda_pca_5.40.5_rf_n3_3_fold
##      Accuracy
## 1 -0.01483438
## 2 -0.01257031
## 3          NA
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_rf.n3_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_rf_n3_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_rf.n3_3_fold
## $probLeft
## [1] NA
## 
## $probRope
## [1] NA
## 
## $probRight
## [1] NA
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_rf.n3_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_rf.n3_3_fold$probLeft/bst_dbf_db_tda_pca_5.40.5_rf.n3_3_fold$probRight
bst_dbf_db_tda_pca_5.40.5_rf.n3_3_fold_odds.left
## [1] NA
# Bayesian Signed Rank Test

#bsr_dbf_db_tda_pca_5.40.5_rf.n3_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_rf_n3_3_fold),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.40.5_rf.n3_3_fold

# Bayesian Correlated Test

#bct_dbf_db_tda_pca_5.40.5_rf.n3_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_rf_n3_3_fold),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.40.5_rf.n2_3_fold

# Rope Plot
#plot(rope(as.matrix(diff_drybean_tda_pca_5.40.5_rf_n3_3_fold),c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_rf.n3_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_rf_n3_3_fold))
#bf_tda_pca_5.40.5_rf.n3_3_fold

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_rf_n3_3_fold))

### Test set diff
diff_drybean_tda_pca_5.40.5_rf.n3_test<-(db_rf_cf_ov_acc-db_tda_pc_5.40.5_n3_rf_cf0_ov_acc)
diff_drybean_tda_pca_5.40.5_rf.n3_test
##  Accuracy 
## 0.4867647
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_rf.n3_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_rf.n3_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_rf.n3_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_rf.n3_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_rf.n3_test$probLeft/bst_dbf_db_tda_pca_5.40.5_rf.n3_test$probRight
bst_dbf_db_tda_pca_5.40.5_rf.n3_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_rf.n3_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_rf.n3_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_rf.n3_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1588333
## 
## $winRight
## [1] 0.8411667
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_rf.n3_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_rf.n3_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_rf.n3_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_rf.n3_test))

#BayesFactor
#bf_tda_pca_5.40.5_rf.n3_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_rf.n3_test)) #bf_tda_pca_5.40.5_rf.n3_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_rf.n2_test)

##Node4

DryBean_TDA_PC_5.40.5_n4_RfFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.40.5.n4.vec, 
                 Importance = T,
                 method = 'rf', 
                 trControl = fitControl,
                 tuneGrid = rfGrid, preProc = c('center','scale'), 
                 metric='Accuracy')
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
DryBean_TDA_PC_5.40.5_n4_RfFit0
## Random Forest 
## 
## 894 samples
##  16 predictor
##   4 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'HOROZ' 
## 
## Pre-processing: centered (16), scaled (16) 
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 596, 596, 596 
## Resampling results across tuning parameters:
## 
##   mtry  Accuracy   Kappa    
##     50  0.9731544  0.9565649
##    100  0.9731544  0.9565649
##    150  0.9731544  0.9565649
##    200  0.9731544  0.9565649
##    250  0.9731544  0.9565649
##    300  0.9731544  0.9565649
##    350  0.9731544  0.9565649
##    400  0.9731544  0.9565649
##    450  0.9731544  0.9565649
##    500  0.9731544  0.9565649
##    550  0.9731544  0.9565649
##    600  0.9731544  0.9565649
##    650  0.9731544  0.9565649
##    700  0.9731544  0.9565649
##    750  0.9720358  0.9547399
##    800  0.9731544  0.9565649
##    850  0.9731544  0.9565649
##    900  0.9731544  0.9565649
##    950  0.9731544  0.9565649
##   1000  0.9742729  0.9583984
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 1000.
DryBean_TDA_PC_5.40.5_n4_RfFit0$resample
##    Accuracy     Kappa Resample
## 1 0.9765101 0.9616854    Fold2
## 2 0.9630872 0.9406299    Fold1
## 3 0.9832215 0.9728800    Fold3
db_tda_pc_5.40.5_n4_rf_fit0_re<-DryBean_TDA_PC_5.40.5_n4_RfFit0$resample[1]


summary(DryBean_TDA_PC_5.40.5_n4_RfFit0)
##                 Length Class      Mode     
## call               5   -none-     call     
## type               1   -none-     character
## predicted        894   factor     numeric  
## err.rate        2500   -none-     numeric  
## confusion         20   -none-     numeric  
## votes           3576   matrix     numeric  
## oob.times        894   -none-     numeric  
## classes            4   -none-     character
## importance        16   -none-     numeric  
## importanceSD       0   -none-     NULL     
## localImportance    0   -none-     NULL     
## proximity          0   -none-     NULL     
## ntree              1   -none-     numeric  
## mtry               1   -none-     numeric  
## forest            14   -none-     list     
## y                894   factor     numeric  
## test               0   -none-     NULL     
## inbag              0   -none-     NULL     
## xNames            16   -none-     character
## problemType        1   -none-     character
## tuneValue          1   data.frame list     
## obsLevels          4   -none-     character
## param              1   -none-     list
vip(DryBean_TDA_PC_5.40.5_n4_RfFit0,25) + ggtitle("Adult_TDA_PCA_5.40.5_n4_RfFit TDA-Assited RF")

# Predict outcome using DryBean_TDA_PC_5.40.5_n4_RfFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.40.5_n4_RfFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.40.5_n4_rf_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in confusionMatrix.default(data = pred0,
## as.factor(Dry_Bean_DatasetTest$Class)): Levels are not in the same order for
## reference and data. Refactoring data to match.
db_tda_pc_5.40.5_n4_rf_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA       92      1    0        0     0     1    0
##   BOMBAY          0    155    0        0     0     0    0
##   CALI          259      0  456        0     5    71    2
##   DERMASON        0      0    0        0     0     0    0
##   HOROZ          45      0   33     1063   573   536  788
##   SEKER           0      0    0        0     0     0    0
##   SIRA            0      0    0        0     0     0    0
## 
## Overall Statistics
##                                           
##                Accuracy : 0.3127          
##                  95% CI : (0.2985, 0.3272)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : 5.244e-14       
##                                           
##                   Kappa : 0.2078          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.23232       0.99359      0.9325          0.0000
## Specificity                  0.99946       1.00000      0.9062          1.0000
## Pos Pred Value               0.97872       1.00000      0.5750             NaN
## Neg Pred Value               0.92373       0.99975      0.9900          0.7395
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.02255       0.03799      0.1118          0.0000
## Detection Prevalence         0.02304       0.03799      0.1944          0.0000
## Balanced Accuracy            0.61589       0.99679      0.9193          0.5000
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9913        0.000      0.0000
## Specificity                0.2961        1.000      1.0000
## Pos Pred Value             0.1886          NaN         NaN
## Neg Pred Value             0.9952        0.851      0.8064
## Prevalence                 0.1417        0.149      0.1936
## Detection Rate             0.1404        0.000      0.0000
## Detection Prevalence       0.7446        0.000      0.0000
## Balanced Accuracy          0.6437        0.500      0.5000
db_tda_pc_5.40.5_n4_rf_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA       92      1    0        0     0     1    0
##   BOMBAY          0    155    0        0     0     0    0
##   CALI          259      0  456        0     5    71    2
##   DERMASON        0      0    0        0     0     0    0
##   HOROZ          45      0   33     1063   573   536  788
##   SEKER           0      0    0        0     0     0    0
##   SIRA            0      0    0        0     0     0    0
## 
## Overall Statistics
##                                           
##                Accuracy : 0.3127          
##                  95% CI : (0.2985, 0.3272)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : 5.244e-14       
##                                           
##                   Kappa : 0.2078          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.23232       0.99359      0.9325          0.0000
## Specificity                  0.99946       1.00000      0.9062          1.0000
## Pos Pred Value               0.97872       1.00000      0.5750             NaN
## Neg Pred Value               0.92373       0.99975      0.9900          0.7395
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.02255       0.03799      0.1118          0.0000
## Detection Prevalence         0.02304       0.03799      0.1944          0.0000
## Balanced Accuracy            0.61589       0.99679      0.9193          0.5000
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9913        0.000      0.0000
## Specificity                0.2961        1.000      1.0000
## Pos Pred Value             0.1886          NaN         NaN
## Neg Pred Value             0.9952        0.851      0.8064
## Prevalence                 0.1417        0.149      0.1936
## Detection Rate             0.1404        0.000      0.0000
## Detection Prevalence       0.7446        0.000      0.0000
## Balanced Accuracy          0.6437        0.500      0.5000
db_tda_pc_5.40.5_n4_rf_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   3.127451e-01   2.078029e-01   2.985351e-01   3.272237e-01   2.605392e-01 
## AccuracyPValue  McnemarPValue 
##   5.244457e-14            NaN
db_tda_pc_5.40.5_n4_rf_cf0_ov_acc<-db_tda_pc_5.40.5_n4_rf_cf0$overall[1]
db_tda_pc_5.40.5_n4_rf_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.2323232   0.9994571      0.9787234      0.9237331 0.9787234
## Class: BOMBAY     0.9935897   1.0000000      1.0000000      0.9997452 1.0000000
## Class: CALI       0.9325153   0.9061543      0.5750315      0.9899605 0.5750315
## Class: DERMASON   0.0000000   1.0000000            NaN      0.7394608        NA
## Class: HOROZ      0.9913495   0.2961165      0.1886109      0.9952015 0.1886109
## Class: SEKER      0.0000000   1.0000000            NaN      0.8509804        NA
## Class: SIRA       0.0000000   1.0000000            NaN      0.8063725        NA
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.2323232 0.3755102 0.09705882     0.02254902
## Class: BOMBAY   0.9935897 0.9967846 0.03823529     0.03799020
## Class: CALI     0.9325153 0.7113885 0.11985294     0.11176471
## Class: DERMASON 0.0000000        NA 0.26053922     0.00000000
## Class: HOROZ    0.9913495 0.3169248 0.14166667     0.14044118
## Class: SEKER    0.0000000        NA 0.14901961     0.00000000
## Class: SIRA     0.0000000        NA 0.19362745     0.00000000
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA           0.02303922         0.6158902
## Class: BOMBAY             0.03799020         0.9967949
## Class: CALI               0.19436275         0.9193348
## Class: DERMASON           0.00000000         0.5000000
## Class: HOROZ              0.74460784         0.6437330
## Class: SEKER              0.00000000         0.5000000
## Class: SIRA               0.00000000         0.5000000
db_tda_pc_5.40.5_n4_rf_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n4_rf_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_pca_5.40.5_rf_n4_3_fold<-(db_rf_fit_re-db_tda_pc_5.40.5_n4_rf_fit0_re)
diff_drybean_tda_pca_5.40.5_rf_n4_3_fold
##      Accuracy
## 1 -0.05425637
## 2 -0.03737116
## 3 -0.06978112
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_rf.n4_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_rf_n4_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_rf.n4_3_fold
## $probLeft
## [1] 0.75
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_rf.n4_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_rf.n4_3_fold$probLeft/bst_dbf_db_tda_pca_5.40.5_rf.n4_3_fold$probRight
bst_dbf_db_tda_pca_5.40.5_rf.n4_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_rf.n4_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_rf_n4_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_rf.n4_3_fold
## $winLeft
## [1] 0.9908
## 
## $winRope
## [1] 0.0092
## 
## $winRight
## [1] 0
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_rf.n4_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_rf_n4_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_rf.n4_3_fold
## $left
## [1] 0.9720913
## 
## $rope
## [1] 0.01415416
## 
## $right
## [1] 0.01375451
# Rope Plot
plot(rope(as.matrix(diff_drybean_tda_pca_5.40.5_rf_n4_3_fold),c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_rf.n4_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_rf_n4_3_fold))
#bf_tda_pca_5.40.5_rf.n4_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.40.5_rf_n4_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_pca_5.40.5_rf_n4_3_fold)
## t = -5.749, df = 2, p-value = 0.02895
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.09407011 -0.01353566
## sample estimates:
##   mean of x 
## -0.05380289
### Test set diff
diff_drybean_tda_pca_5.40.5_rf.n4_test<-(db_rf_cf_ov_acc-db_tda_pc_5.40.5_n4_rf_cf0_ov_acc)
diff_drybean_tda_pca_5.40.5_rf.n4_test
##  Accuracy 
## 0.6112745
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_rf.n4_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_rf.n4_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_rf.n4_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_rf.n4_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_rf.n4_test$probLeft/bst_dbf_db_tda_pca_5.40.5_rf.n4_test$probRight
bst_dbf_db_tda_pca_5.40.5_rf.n4_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_rf.n4_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_rf.n4_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_rf.n4_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1589333
## 
## $winRight
## [1] 0.8410667
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_rf.n4_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_rf.n4_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_rf.n4_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_rf.n4_test))

#BayesFactor
#bf_tda_pca_5.40.5_rf.n4_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_rf.n4_test)) #bf_tda_pca_5.40.5_rf.n4_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_rf.n4_test))

##Node5

#DryBean_TDA_PC_5.40.5_n5_RfFit0 <- train(as.factor(Class) ~ ., data = #tda.m_dry_bean_dataset_5.40.5.n5.vec, 
#                 Importance = T,
#                 method = 'rf', 
#                 trControl = fitControl,
#                 tuneGrid = rfGrid, preProc = c('center','scale'), 
#                 metric='Accuracy')

#DryBean_TDA_PC_5.40.5_n5_RfFit0
#DryBean_TDA_PC_5.40.5_n5_RfFit0$resample
#db_tda_pc_5.40.5_n5_rf_fit0_re<-DryBean_TDA_PC_5.40.5_n5_RfFit0$resample[1]


#summary(DryBean_TDA_PC_5.40.5_n5_RfFit0)

#vip(DryBean_TDA_PC_5.40.5_n5_RfFit0,25) + ggtitle("Adult_TDA_PCA_5.40.5_n5_RfFit TDA-Assited RF")



# Predict outcome using DryBean_TDA_PC_5.40.5_n5_RfFit0 from training data based on testing data
#pred0 <- predict(DryBean_TDA_PC_5.40.5_n5_RfFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
#db_tda_pc_5.40.5_n5_rf_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
#db_tda_pc_5.40.5_n5_rf_cf0
#db_tda_pc_5.40.5_n5_rf_cf0 
#db_tda_pc_5.40.5_n5_rf_cf0$overall
#db_tda_pc_5.40.5_n5_rf_cf0_ov_acc<-db_tda_pc_5.40.5_n5_rf_cf0$overall[1]
#db_tda_pc_5.40.5_n5_rf_cf0$byClass
#db_tda_pc_5.40.5_n5_rf_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n5_rf_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

#diff_drybean_tda_pca_5.40.5_rf_n5_3_fold<-(db_rf_fit_re-db_tda_pc_5.40.5_n5_rf_fit0_re)
#diff_drybean_tda_pca_5.40.5_rf_n5_3_fold

## Bayesian Tests 3-fold diff

# Bayesian Sign Test

#bst_dbf_db_tda_pca_5.40.5_rf.n5_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_rf_n5_3_fold),-0.01,0.01)
#bst_dbf_db_tda_pca_5.40.5_rf.n5_3_fold

# Odds Left Bayesian Sign Test 

#bst_dbf_db_tda_pca_5.40.5_rf.n5_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_rf.n5_3_fold$probLeft/bst_dbf_db_tda_pca_5.40.5_rf.n5_3_fold$probRight
#bst_dbf_db_tda_pca_5.40.5_rf.n5_3_fold_odds.left

# Bayesian Signed Rank Test

#bsr_dbf_db_tda_pca_5.40.5_rf.n5_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_rf_n5_3_fold),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.40.5_rf.n5_3_fold

# Bayesian Correlated Test

#bct_dbf_db_tda_pca_5.40.5_rf.n5_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_rf_n5_3_fold),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.40.5_rf.n5_3_fold

# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_rf_n5_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_rf.n5_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_rf_n5_3_fold))
#bf_tda_pca_5.40.5_rf.n5_3_fold

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_rf_n5_3_fold))

### Test set diff
#diff_drybean_tda_pca_5.40.5_rf.n5_test<-(db_rf_cf_ov_acc-db_tda_pc_5.40.5_n5_rf_cf0_ov_acc)
#diff_drybean_tda_pca_5.40.5_rf.n5_test


## Bayesian Tests Test set diff

# Bayesian Sign Test

#bst_dbf_db_tda_pca_5.40.5_rf.n5_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_rf.n5_test),-0.01,0.01)
#bst_dbf_db_tda_pca_5.40.5_rf.n5_test

# Odds Left Bayesian Sign Test 

#bst_dbf_db_tda_pca_5.40.5_rf.n5_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_rf.n5_test$probLeft/bst_dbf_db_tda_pca_5.40.5_rf.n5_test$probRight
#bst_dbf_db_tda_pca_5.40.5_rf.n5_test_odds.left

# Bayesian Signed Rank Test

#bsr_dbf_db_tda_pca_5.40.5_rf.n5_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_rf.n5_test),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.40.5_rf.n5_test

# Bayesian Correlated Test

#bct_dbf_db_tda_pca_5.40.5_rf.n5_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_rf.n5_test),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.40.5_rf.n5_test

# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_rf.n5_test))

#BayesFactor
#bf_tda_pca_5.40.5_rf.n5_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_rf.n5_test)) #bf_tda_pca_5.40.5_rf.n5_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_rf.n5_test))

##With TDA KDE filter 5 intervals, 50% overlap, 5 bins 
##Node1

DryBean_TDA_KDE_5.40.5_n1_RfFit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.40.5.n1.vec, 
                 Importance = T,
                 method = 'rf', 
                 trControl = fitControl,
                 tuneGrid = rfGrid, preProc = c('center','scale'), 
                 metric='Accuracy')
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
DryBean_TDA_KDE_5.40.5_n1_RfFit0
## Random Forest 
## 
## 7503 samples
##   16 predictor
##    7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## Pre-processing: centered (16), scaled (16) 
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 5002, 5002, 5002 
## Resampling results across tuning parameters:
## 
##   mtry  Accuracy   Kappa    
##     50  0.9462882  0.9352868
##    100  0.9468213  0.9359354
##    150  0.9473544  0.9365736
##    200  0.9466880  0.9357703
##    250  0.9461549  0.9351244
##    300  0.9470878  0.9362528
##    350  0.9457550  0.9346471
##    400  0.9460216  0.9349740
##    450  0.9470878  0.9362530
##    500  0.9461549  0.9351235
##    550  0.9458883  0.9348097
##    600  0.9468213  0.9359354
##    650  0.9456218  0.9344775
##    700  0.9458883  0.9348037
##    750  0.9470878  0.9362523
##    800  0.9462882  0.9352864
##    850  0.9474877  0.9367392
##    900  0.9469546  0.9360866
##    950  0.9466880  0.9357708
##   1000  0.9460216  0.9349682
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 850.
DryBean_TDA_KDE_5.40.5_n1_RfFit0$resample
##    Accuracy     Kappa Resample
## 1 0.9436226 0.9320860    Fold3
## 2 0.9500200 0.9398229    Fold2
## 3 0.9488205 0.9383085    Fold1
ad_tda_kde_5.40.5_n1_rf_fit0_re<-DryBean_TDA_KDE_5.40.5_n1_RfFit0$resample[1]


summary(DryBean_TDA_KDE_5.40.5_n1_RfFit0)
##                 Length Class      Mode     
## call                5  -none-     call     
## type                1  -none-     character
## predicted        7503  factor     numeric  
## err.rate         4000  -none-     numeric  
## confusion          56  -none-     numeric  
## votes           52521  matrix     numeric  
## oob.times        7503  -none-     numeric  
## classes             7  -none-     character
## importance         16  -none-     numeric  
## importanceSD        0  -none-     NULL     
## localImportance     0  -none-     NULL     
## proximity           0  -none-     NULL     
## ntree               1  -none-     numeric  
## mtry                1  -none-     numeric  
## forest             14  -none-     list     
## y                7503  factor     numeric  
## test                0  -none-     NULL     
## inbag               0  -none-     NULL     
## xNames             16  -none-     character
## problemType         1  -none-     character
## tuneValue           1  data.frame list     
## obsLevels           7  -none-     character
## param               1  -none-     list
vip(DryBean_TDA_KDE_5.40.5_n1_RfFit0,25) + ggtitle("DryBean_TDA_KDE_5.40.5_n1_RfFit TDA-Assited RF")

# Predict outcome using DryBean_TDA_KDE_5.40.5_n1_RfFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.40.5_n1_RfFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
ad_tda_kde_5.40.5_n1_rf_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
ad_tda_kde_5.40.5_n1_rf_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      391      0    0        1     0     0    3
##   BOMBAY          0    156    0        0     0     0    0
##   CALI            0      0  486        0     0     0    0
##   DERMASON        1      0    0      801     4     3   45
##   HOROZ           0      0    1        3   572     0    2
##   SEKER           0      0    0       36     0   592   11
##   SIRA            4      0    2      222     2    13  729
## 
## Overall Statistics
##                                           
##                Accuracy : 0.9135          
##                  95% CI : (0.9044, 0.9219)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.8959          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.98737       1.00000      0.9939          0.7535
## Specificity                  0.99891       1.00000      1.0000          0.9824
## Pos Pred Value               0.98987       1.00000      1.0000          0.9379
## Neg Pred Value               0.99864       1.00000      0.9992          0.9188
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.09583       0.03824      0.1191          0.1963
## Detection Prevalence         0.09681       0.03824      0.1191          0.2093
## Balanced Accuracy            0.99314       1.00000      0.9969          0.8680
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9896       0.9737      0.9228
## Specificity                0.9983       0.9865      0.9261
## Pos Pred Value             0.9896       0.9264      0.7500
## Neg Pred Value             0.9983       0.9954      0.9804
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1402       0.1451      0.1787
## Detection Prevalence       0.1417       0.1566      0.2382
## Balanced Accuracy          0.9940       0.9801      0.9245
ad_tda_kde_5.40.5_n1_rf_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      391      0    0        1     0     0    3
##   BOMBAY          0    156    0        0     0     0    0
##   CALI            0      0  486        0     0     0    0
##   DERMASON        1      0    0      801     4     3   45
##   HOROZ           0      0    1        3   572     0    2
##   SEKER           0      0    0       36     0   592   11
##   SIRA            4      0    2      222     2    13  729
## 
## Overall Statistics
##                                           
##                Accuracy : 0.9135          
##                  95% CI : (0.9044, 0.9219)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.8959          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.98737       1.00000      0.9939          0.7535
## Specificity                  0.99891       1.00000      1.0000          0.9824
## Pos Pred Value               0.98987       1.00000      1.0000          0.9379
## Neg Pred Value               0.99864       1.00000      0.9992          0.9188
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.09583       0.03824      0.1191          0.1963
## Detection Prevalence         0.09681       0.03824      0.1191          0.2093
## Balanced Accuracy            0.99314       1.00000      0.9969          0.8680
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9896       0.9737      0.9228
## Specificity                0.9983       0.9865      0.9261
## Pos Pred Value             0.9896       0.9264      0.7500
## Neg Pred Value             0.9983       0.9954      0.9804
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1402       0.1451      0.1787
## Detection Prevalence       0.1417       0.1566      0.2382
## Balanced Accuracy          0.9940       0.9801      0.9245
ad_tda_kde_5.40.5_n1_rf_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.9134804      0.8958590      0.9044325      0.9219309      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
ad_tda_kde_5.40.5_n1_rf_cf0_ov_acc<-ad_tda_kde_5.40.5_n1_rf_cf0$overall[1]
ad_tda_kde_5.40.5_n1_rf_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.9873737   0.9989142      0.9898734      0.9986431 0.9898734
## Class: BOMBAY     1.0000000   1.0000000      1.0000000      1.0000000 1.0000000
## Class: CALI       0.9938650   1.0000000      1.0000000      0.9991653 1.0000000
## Class: DERMASON   0.7535278   0.9824329      0.9379391      0.9187849 0.9379391
## Class: HOROZ      0.9896194   0.9982867      0.9896194      0.9982867 0.9896194
## Class: SEKER      0.9736842   0.9864631      0.9264476      0.9953502 0.9264476
## Class: SIRA       0.9227848   0.9261398      0.7500000      0.9803732 0.7500000
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.9873737 0.9886220 0.09705882     0.09583333
## Class: BOMBAY   1.0000000 1.0000000 0.03823529     0.03823529
## Class: CALI     0.9938650 0.9969231 0.11985294     0.11911765
## Class: DERMASON 0.7535278 0.8356808 0.26053922     0.19632353
## Class: HOROZ    0.9896194 0.9896194 0.14166667     0.14019608
## Class: SEKER    0.9736842 0.9494787 0.14901961     0.14509804
## Class: SIRA     0.9227848 0.8274688 0.19362745     0.17867647
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA           0.09681373         0.9931440
## Class: BOMBAY             0.03823529         1.0000000
## Class: CALI               0.11911765         0.9969325
## Class: DERMASON           0.20931373         0.8679803
## Class: HOROZ              0.14166667         0.9939530
## Class: SEKER              0.15661765         0.9800737
## Class: SIRA               0.23823529         0.9244623
ad_tda_kde_5.40.5_n1_rf_cf0_pre_rec_f1<-ad_tda_kde_5.40.5_n1_rf_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_kde_5.40.5_rf_n1_3_fold<-(db_rf_fit_re-ad_tda_kde_5.40.5_n1_rf_fit0_re)
diff_drybean_tda_kde_5.40.5_rf_n1_3_fold
##      Accuracy
## 1 -0.02136885
## 2 -0.02430391
## 3 -0.03538012
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_rf.n1_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_rf_n1_3_fold),-0.01,0.01)
bst_tda_kde_5.40.5_rf.n1_3_fold
## $probLeft
## [1] 0.75
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_rf.n1_3_fold_odds.left<-bst_tda_kde_5.40.5_rf.n1_3_fold$probLeft/bst_tda_kde_5.40.5_rf.n1_3_fold$probRight
bst_tda_kde_5.40.5_rf.n1_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_rf.n1_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_rf_n1_3_fold),-0.01,0.01)
bsr_tda_kde_5.40.5_rf.n1_3_fold
## $winLeft
## [1] 0.9912667
## 
## $winRope
## [1] 0.008733333
## 
## $winRight
## [1] 0
# Bayesian Correlated Test

bct_tda_kde_5.40.5_rf.n1_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_rf_n1_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_rf.n1_3_fold
## $left
## [1] 0.9627262
## 
## $rope
## [1] 0.02864751
## 
## $right
## [1] 0.00862633
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.40.5_rf_n1_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_kde_5.40.5_rf.n1_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_rf_n1_3_fold))
#bf_tda_kde_5.40.5_rf.n1_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.40.5_rf_n1_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_kde_5.40.5_rf_n1_3_fold)
## t = -6.3329, df = 2, p-value = 0.02404
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.045373724 -0.008661529
## sample estimates:
##   mean of x 
## -0.02701763
### Test set diff
diff_drybean_tda_kde_5.40.5_rf.n1_test<-(db_rf_cf_ov_acc-ad_tda_kde_5.40.5_n1_rf_cf0_ov_acc)
diff_drybean_tda_kde_5.40.5_rf.n1_test
##   Accuracy 
## 0.01053922
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_rf.n1_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_rf.n1_test),-0.01,0.01)
bst_tda_kde_5.40.5_rf.n1_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_rf.n1_test_odds.left<-bst_tda_kde_5.40.5_rf.n1_test$probLeft/bst_tda_kde_5.40.5_rf.n1_test$probRight
bst_tda_kde_5.40.5_rf.n1_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_rf.n1_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_rf.n1_test),-0.01,0.01)
bsr_tda_kde_5.40.5_rf.n1_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.4577
## 
## $winRight
## [1] 0.5423
# Bayesian Correlated Test

bct_tda_kde_5.40.5_rf.n1_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_rf.n1_test),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_rf.n1_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_rf.n1_test))

#BayesFactor
#bf_tda_kde_5.40.5_rf.n1_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_rf.n1_test)) #bf_tda_kde_5.40.5_rf.n1_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_rf.n1_test))

##Node2

DryBean_TDA_KDE_5.40.5_n2_RfFit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.40.5.n2.vec, 
                 Importance = T,
                 method = 'rf', 
                 trControl = fitControl,
                 tuneGrid = rfGrid, preProc = c('center','scale'),                    
                 metric='Accuracy')
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
DryBean_TDA_KDE_5.40.5_n2_RfFit0
## Random Forest 
## 
## 7002 samples
##   16 predictor
##    6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## Pre-processing: centered (16), scaled (16) 
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 4667, 4669, 4668 
## Resampling results across tuning parameters:
## 
##   mtry  Accuracy   Kappa    
##     50  0.9444442  0.9285797
##    100  0.9450154  0.9293120
##    150  0.9444438  0.9285704
##    200  0.9455864  0.9300511
##    250  0.9447294  0.9289374
##    300  0.9444440  0.9285760
##    350  0.9460147  0.9305963
##    400  0.9444441  0.9285848
##    450  0.9445866  0.9287581
##    500  0.9453010  0.9296870
##    550  0.9458722  0.9304104
##    600  0.9453009  0.9296828
##    650  0.9453010  0.9296819
##    700  0.9457292  0.9302281
##    750  0.9457297  0.9302372
##    800  0.9454438  0.9298669
##    850  0.9455865  0.9300402
##    900  0.9460147  0.9305929
##    950  0.9457292  0.9302317
##   1000  0.9451583  0.9295105
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 350.
DryBean_TDA_KDE_5.40.5_n2_RfFit0$resample
##    Accuracy     Kappa Resample
## 1 0.9468495 0.9315144    Fold2
## 2 0.9516060 0.9378969    Fold1
## 3 0.9395887 0.9223775    Fold3
ad_tda_KDE_5.40.5_n2_rf_fit0_re<-DryBean_TDA_KDE_5.40.5_n2_RfFit0$resample[1]


summary(DryBean_TDA_KDE_5.40.5_n2_RfFit0)
##                 Length Class      Mode     
## call                5  -none-     call     
## type                1  -none-     character
## predicted        7002  factor     numeric  
## err.rate         3500  -none-     numeric  
## confusion          42  -none-     numeric  
## votes           42012  matrix     numeric  
## oob.times        7002  -none-     numeric  
## classes             6  -none-     character
## importance         16  -none-     numeric  
## importanceSD        0  -none-     NULL     
## localImportance     0  -none-     NULL     
## proximity           0  -none-     NULL     
## ntree               1  -none-     numeric  
## mtry                1  -none-     numeric  
## forest             14  -none-     list     
## y                7002  factor     numeric  
## test                0  -none-     NULL     
## inbag               0  -none-     NULL     
## xNames             16  -none-     character
## problemType         1  -none-     character
## tuneValue           1  data.frame list     
## obsLevels           6  -none-     character
## param               1  -none-     list
vip(DryBean_TDA_KDE_5.40.5_n2_RfFit0,25) + ggtitle("DryBean_TDA_KDE_5.40.5_n2_RfFit TDA-Assited RF")

# Predict outcome using DryBean_TDA_KDE_5.40.5_n2_RfFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.40.5_n2_RfFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
ad_tda_kde_5.40.5_n2_rf_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in confusionMatrix.default(data = pred0,
## as.factor(Dry_Bean_DatasetTest$Class)): Levels are not in the same order for
## reference and data. Refactoring data to match.
ad_tda_kde_5.40.5_n2_rf_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      330     12   17        0    10     1    2
##   BOMBAY          0      0    0        0     0     0    0
##   CALI           20    142  462        0    14     0    1
##   DERMASON        1      0    0     1029     3    16   96
##   HOROZ           1      0    3        1   551     0    2
##   SEKER          36      2    1        8     0   586    7
##   SIRA            8      0    6       25     0     5  682
## 
## Overall Statistics
##                                           
##                Accuracy : 0.8922          
##                  95% CI : (0.8822, 0.9015)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.8688          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.83333       0.00000      0.9448          0.9680
## Specificity                  0.98860       1.00000      0.9507          0.9616
## Pos Pred Value               0.88710           NaN      0.7230          0.8987
## Neg Pred Value               0.98220       0.96176      0.9922          0.9884
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.08088       0.00000      0.1132          0.2522
## Detection Prevalence         0.09118       0.00000      0.1566          0.2806
## Balanced Accuracy            0.91097       0.50000      0.9477          0.9648
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9533       0.9638      0.8633
## Specificity                0.9980       0.9844      0.9866
## Pos Pred Value             0.9875       0.9156      0.9394
## Neg Pred Value             0.9923       0.9936      0.9678
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1350       0.1436      0.1672
## Detection Prevalence       0.1368       0.1569      0.1779
## Balanced Accuracy          0.9756       0.9741      0.9250
ad_tda_kde_5.40.5_n2_rf_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      330     12   17        0    10     1    2
##   BOMBAY          0      0    0        0     0     0    0
##   CALI           20    142  462        0    14     0    1
##   DERMASON        1      0    0     1029     3    16   96
##   HOROZ           1      0    3        1   551     0    2
##   SEKER          36      2    1        8     0   586    7
##   SIRA            8      0    6       25     0     5  682
## 
## Overall Statistics
##                                           
##                Accuracy : 0.8922          
##                  95% CI : (0.8822, 0.9015)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.8688          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.83333       0.00000      0.9448          0.9680
## Specificity                  0.98860       1.00000      0.9507          0.9616
## Pos Pred Value               0.88710           NaN      0.7230          0.8987
## Neg Pred Value               0.98220       0.96176      0.9922          0.9884
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.08088       0.00000      0.1132          0.2522
## Detection Prevalence         0.09118       0.00000      0.1566          0.2806
## Balanced Accuracy            0.91097       0.50000      0.9477          0.9648
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9533       0.9638      0.8633
## Specificity                0.9980       0.9844      0.9866
## Pos Pred Value             0.9875       0.9156      0.9394
## Neg Pred Value             0.9923       0.9936      0.9678
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1350       0.1436      0.1672
## Detection Prevalence       0.1368       0.1569      0.1779
## Balanced Accuracy          0.9756       0.9741      0.9250
ad_tda_kde_5.40.5_n2_rf_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.8921569      0.8688131      0.8822342      0.9015139      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
ad_tda_kde_5.40.5_n2_rf_cf0_ov_acc<-ad_tda_kde_5.40.5_n2_rf_cf0$overall[1]
ad_tda_kde_5.40.5_n2_rf_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.8333333   0.9885993      0.8870968      0.9822006 0.8870968
## Class: BOMBAY     0.0000000   1.0000000            NaN      0.9617647        NA
## Class: CALI       0.9447853   0.9507101      0.7230047      0.9921534 0.7230047
## Class: DERMASON   0.9680151   0.9615512      0.8986900      0.9884157 0.8986900
## Class: HOROZ      0.9532872   0.9980011      0.9874552      0.9923339 0.9874552
## Class: SEKER      0.9638158   0.9844470      0.9156250      0.9936047 0.9156250
## Class: SIRA       0.8632911   0.9866261      0.9393939      0.9677996 0.9393939
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.8333333 0.8593750 0.09705882     0.08088235
## Class: BOMBAY   0.0000000        NA 0.03823529     0.00000000
## Class: CALI     0.9447853 0.8191489 0.11985294     0.11323529
## Class: DERMASON 0.9680151 0.9320652 0.26053922     0.25220588
## Class: HOROZ    0.9532872 0.9700704 0.14166667     0.13504902
## Class: SEKER    0.9638158 0.9391026 0.14901961     0.14362745
## Class: SIRA     0.8632911 0.8997361 0.19362745     0.16715686
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA           0.09117647         0.9109663
## Class: BOMBAY             0.00000000         0.5000000
## Class: CALI               0.15661765         0.9477477
## Class: DERMASON           0.28063725         0.9647831
## Class: HOROZ              0.13676471         0.9756442
## Class: SEKER              0.15686275         0.9741314
## Class: SIRA               0.17794118         0.9249586
ad_tda_kde_5.40.5_n2_rf_cf0_pre_rec_f1<-ad_tda_kde_5.40.5_n2_rf_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_kde_5.40.5_rf_n2_3_fold<-(db_rf_fit_re-ad_tda_KDE_5.40.5_n2_rf_fit0_re)
diff_drybean_tda_kde_5.40.5_rf_n2_3_fold
##      Accuracy
## 1 -0.02459585
## 2 -0.02588991
## 3 -0.02614834
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_rf.n2_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_rf_n2_3_fold),-0.01,0.01)
bst_tda_kde_5.40.5_rf.n2_3_fold
## $probLeft
## [1] 0.75
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_rf.n2_3_fold_odds.left<-bst_tda_kde_5.40.5_rf.n2_3_fold$probLeft/bst_tda_kde_5.40.5_rf.n2_3_fold$probRight
bst_tda_kde_5.40.5_rf.n2_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_rf.n2_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_rf_n2_3_fold),-0.01,0.01)
bsr_tda_kde_5.40.5_rf.n2_3_fold
## $winLeft
## [1] 0.9916333
## 
## $winRope
## [1] 0.008366667
## 
## $winRight
## [1] 0
# Bayesian Correlated Test

bct_tda_kde_5.40.5_rf.n2_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_rf_n2_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_rf.n2_3_fold
## $left
## [1] 0.9993649
## 
## $rope
## [1] 0.0005134639
## 
## $right
## [1] 0.0001216583
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.40.5_rf_n2_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_kde_5.40.5_rf.n2_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_rf_n2_3_fold))
#bf_tda_kde_5.40.5_rf.n2_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.40.5_rf_n2_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_kde_5.40.5_rf_n2_3_fold)
## t = -53.19, df = 2, p-value = 0.0003533
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.02761106 -0.02347834
## sample estimates:
##  mean of x 
## -0.0255447
### Test set diff
diff_drybean_tda_kde_5.40.5_rf.n2_test<-(db_rf_cf_ov_acc-ad_tda_kde_5.40.5_n2_rf_cf0_ov_acc)
diff_drybean_tda_kde_5.40.5_rf.n2_test
##   Accuracy 
## 0.03186275
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_rf.n2_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_rf.n2_test),-0.01,0.01)
bst_tda_kde_5.40.5_rf.n2_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_rf.n2_test_odds.left<-bst_tda_kde_5.40.5_rf.n2_test$probLeft/bst_tda_kde_5.40.5_rf.n2_test$probRight
bst_tda_kde_5.40.5_rf.n2_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_rf.n2_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_rf.n2_test),-0.01,0.01)
bsr_tda_kde_5.40.5_rf.n2_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1591
## 
## $winRight
## [1] 0.8409
# Bayesian Correlated Test

bct_tda_kde_5.40.5_rf.n2_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_rf.n2_test),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_rf.n2_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_rf.n2_test))

#BayesFactor
#bf_tda_kde_5.40.5_rf.n2_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_rf.n2_test)) #bf_tda_kde_5.40.5_rf.n2_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_rf.n2_test))

##Node3

DryBean_TDA_KDE_5.40.5_n3_RfFit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.40.5.n3.vec, 
                 Importance = T,
                 method = 'rf', 
                 trControl = fitControl,
                 tuneGrid = rfGrid, preProc = c('center','scale'),                    
                 metric='Accuracy')
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry=  50 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 100 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 150 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 200 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 250 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 300 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 350 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 400 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 450 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 500 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 550 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 600 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 650 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 700 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 750 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 800 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 850 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 900 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 950 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry=1000 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
DryBean_TDA_KDE_5.40.5_n3_RfFit0
## Random Forest 
## 
## 3511 samples
##   16 predictor
##    6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## Pre-processing: centered (16), scaled (16) 
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 2342, 2339, 2341 
## Resampling results across tuning parameters:
## 
##   mtry  Accuracy   Kappa    
##     50  0.9097929  0.8633737
##    100  0.9085108  0.8613713
##    150  0.9085101  0.8613397
##    200  0.9089385  0.8620215
##    250  0.9089378  0.8619806
##    300  0.9080827  0.8606882
##    350  0.9080838  0.8607785
##    400  0.9072280  0.8594751
##    450  0.9080827  0.8607326
##    500  0.9046628  0.8556050
##    550  0.9080835  0.8607980
##    600  0.9059456  0.8574932
##    650  0.9093655  0.8626489
##    700  0.9093666  0.8626688
##    750  0.9097932  0.8632842
##    800  0.9072273  0.8594473
##    850  0.9076561  0.8600941
##    900  0.9068003  0.8587768
##    950  0.9063722  0.8581167
##   1000  0.9093662  0.8626360
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 750.
DryBean_TDA_KDE_5.40.5_n3_RfFit0$resample
##    Accuracy     Kappa Resample
## 1 0.9161677 0.8730770    Fold1
## 2 0.9034188 0.8534913    Fold3
## 3        NA        NA    Fold2
ad_tda_kde_5.40.5_n3_rf_fit0_re<-DryBean_TDA_KDE_5.40.5_n3_RfFit0$resample[1]


summary(DryBean_TDA_KDE_5.40.5_n3_RfFit0)
##                 Length Class      Mode     
## call                5  -none-     call     
## type                1  -none-     character
## predicted        3511  factor     numeric  
## err.rate         3500  -none-     numeric  
## confusion          42  -none-     numeric  
## votes           21066  matrix     numeric  
## oob.times        3511  -none-     numeric  
## classes             6  -none-     character
## importance         16  -none-     numeric  
## importanceSD        0  -none-     NULL     
## localImportance     0  -none-     NULL     
## proximity           0  -none-     NULL     
## ntree               1  -none-     numeric  
## mtry                1  -none-     numeric  
## forest             14  -none-     list     
## y                3511  factor     numeric  
## test                0  -none-     NULL     
## inbag               0  -none-     NULL     
## xNames             16  -none-     character
## problemType         1  -none-     character
## tuneValue           1  data.frame list     
## obsLevels           6  -none-     character
## param               1  -none-     list
vip(DryBean_TDA_KDE_5.40.5_n3_RfFit0,25) + ggtitle("DryBean_TDA_KDE_5.40.5_n3_RfFit TDA-Assited RF")

# Predict outcome using DryBean_TDA_KDE_5.40.5_n3_RfFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.40.5_n3_RfFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
ad_tda_kde_5.40.5_n3_rf_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in confusionMatrix.default(data = pred0,
## as.factor(Dry_Bean_DatasetTest$Class)): Levels are not in the same order for
## reference and data. Refactoring data to match.
ad_tda_kde_5.40.5_n3_rf_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA       45      9    4        0     2     3    5
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    1        0     0     0    0
##   DERMASON        0      0    0     1051     8    14   69
##   HOROZ         233      8  131        1   509     0    3
##   SEKER          17      3    1        3     0   577    8
##   SIRA          101    136  352        8    59    14  705
## 
## Overall Statistics
##                                           
##                Accuracy : 0.7078          
##                  95% CI : (0.6936, 0.7218)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.6381          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.11364       0.00000   0.0020450          0.9887
## Specificity                  0.99376       1.00000   1.0000000          0.9698
## Pos Pred Value               0.66176           NaN   1.0000000          0.9203
## Neg Pred Value               0.91251       0.96176   0.8803628          0.9959
## Prevalence                   0.09706       0.03824   0.1198529          0.2605
## Detection Rate               0.01103       0.00000   0.0002451          0.2576
## Detection Prevalence         0.01667       0.00000   0.0002451          0.2799
## Balanced Accuracy            0.55370       0.50000   0.5010225          0.9793
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.8806       0.9490      0.8924
## Specificity                0.8926       0.9908      0.7964
## Pos Pred Value             0.5751       0.9475      0.5127
## Neg Pred Value             0.9784       0.9911      0.9686
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1248       0.1414      0.1728
## Detection Prevalence       0.2169       0.1493      0.3370
## Balanced Accuracy          0.8866       0.9699      0.8444
ad_tda_kde_5.40.5_n3_rf_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA       45      9    4        0     2     3    5
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    1        0     0     0    0
##   DERMASON        0      0    0     1051     8    14   69
##   HOROZ         233      8  131        1   509     0    3
##   SEKER          17      3    1        3     0   577    8
##   SIRA          101    136  352        8    59    14  705
## 
## Overall Statistics
##                                           
##                Accuracy : 0.7078          
##                  95% CI : (0.6936, 0.7218)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.6381          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.11364       0.00000   0.0020450          0.9887
## Specificity                  0.99376       1.00000   1.0000000          0.9698
## Pos Pred Value               0.66176           NaN   1.0000000          0.9203
## Neg Pred Value               0.91251       0.96176   0.8803628          0.9959
## Prevalence                   0.09706       0.03824   0.1198529          0.2605
## Detection Rate               0.01103       0.00000   0.0002451          0.2576
## Detection Prevalence         0.01667       0.00000   0.0002451          0.2799
## Balanced Accuracy            0.55370       0.50000   0.5010225          0.9793
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.8806       0.9490      0.8924
## Specificity                0.8926       0.9908      0.7964
## Pos Pred Value             0.5751       0.9475      0.5127
## Neg Pred Value             0.9784       0.9911      0.9686
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1248       0.1414      0.1728
## Detection Prevalence       0.2169       0.1493      0.3370
## Balanced Accuracy          0.8866       0.9699      0.8444
ad_tda_kde_5.40.5_n3_rf_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.7078431      0.6380617      0.6936214      0.7217667      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
ad_tda_kde_5.40.5_n3_rf_cf0_ov_acc<-ad_tda_kde_5.40.5_n3_rf_cf0$overall[1]
ad_tda_kde_5.40.5_n3_rf_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA  0.11363636   0.9937568      0.6617647      0.9125125 0.6617647
## Class: BOMBAY    0.00000000   1.0000000            NaN      0.9617647        NA
## Class: CALI      0.00204499   1.0000000      1.0000000      0.8803628 1.0000000
## Class: DERMASON  0.98871119   0.9698376      0.9203152      0.9959156 0.9203152
## Class: HOROZ     0.88062284   0.8926328      0.5751412      0.9784038 0.5751412
## Class: SEKER     0.94901316   0.9907834      0.9474548      0.9910689 0.9474548
## Class: SIRA      0.89240506   0.7963526      0.5127273      0.9685767 0.5127273
##                     Recall          F1 Prevalence Detection Rate
## Class: BARBUNYA 0.11363636 0.193965517 0.09705882    0.011029412
## Class: BOMBAY   0.00000000          NA 0.03823529    0.000000000
## Class: CALI     0.00204499 0.004081633 0.11985294    0.000245098
## Class: DERMASON 0.98871119 0.953287982 0.26053922    0.257598039
## Class: HOROZ    0.88062284 0.695830485 0.14166667    0.124754902
## Class: SEKER    0.94901316 0.948233361 0.14901961    0.141421569
## Class: SIRA     0.89240506 0.651270208 0.19362745    0.172794118
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA          0.016666667         0.5536966
## Class: BOMBAY            0.000000000         0.5000000
## Class: CALI              0.000245098         0.5010225
## Class: DERMASON          0.279901961         0.9792744
## Class: HOROZ             0.216911765         0.8866278
## Class: SEKER             0.149264706         0.9698983
## Class: SIRA              0.337009804         0.8443788
ad_tda_kde_5.40.5_n3_rf_cf0_pre_rec_f1<-ad_tda_kde_5.40.5_n3_rf_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_kde_5.40.5_rf_n3_3_fold<-(db_rf_fit_re-ad_tda_kde_5.40.5_n3_rf_fit0_re)
diff_drybean_tda_kde_5.40.5_rf_n3_3_fold
##      Accuracy
## 1 0.006086034
## 2 0.022297281
## 3          NA
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_rf.n3_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_rf_n3_3_fold),-0.01,0.01)
bst_tda_kde_5.40.5_rf.n3_3_fold
## $probLeft
## [1] NA
## 
## $probRope
## [1] NA
## 
## $probRight
## [1] NA
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_rf.n3_3_fold_odds.left<-bst_tda_kde_5.40.5_rf.n3_3_fold$probLeft/bst_tda_kde_5.40.5_rf.n3_3_fold$probRight
bst_tda_kde_5.40.5_rf.n3_3_fold_odds.left
## [1] NA
# Bayesian Signed Rank Test

#bsr_tda_kde_5.40.5_rf.n3_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_rf_n3_3_fold),-0.01,0.01)
#bsr_tda_kde_5.40.5_rf.n3_3_fold

# Bayesian Correlated Test

#bct_tda_kde_5.40.5_rf.n3_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_rf_n3_3_fold),0.1,-0.01,0.01)
#bct_tda_kde_5.40.5_rf.n3_3_fold

# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_rf_n3_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_kde_5.40.5_rf.n3_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_rf_n3_3_fold))
#bf_tda_kde_5.40.5_rf.n3_3_fold

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_rf_n3_3_fold))


### Test set diff
diff_drybean_tda_kde_5.40.5_rf.n3_test<-(db_rf_cf_ov_acc-ad_tda_kde_5.40.5_n3_rf_cf0_ov_acc)
diff_drybean_tda_kde_5.40.5_rf.n3_test
##  Accuracy 
## 0.2161765
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_rf.n3_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_rf.n3_test),-0.01,0.01)
bst_tda_kde_5.40.5_rf.n3_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_rf.n3_test_odds.left<-bst_tda_kde_5.40.5_rf.n3_test$probLeft/bst_tda_kde_5.40.5_rf.n3_test$probRight
bst_tda_kde_5.40.5_rf.n3_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_rf.n3_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_rf.n3_test),-0.01,0.01)
bsr_tda_kde_5.40.5_rf.n3_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1599
## 
## $winRight
## [1] 0.8401
# Bayesian Correlated Test

bct_tda_kde_5.40.5_rf.n3_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_rf.n3_test),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_rf.n3_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_rf.n3_test))

#BayesFactor
#bf_tda_kde_5.40.5_rf.n3_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_rf.n3_test)) #bf_tda_kde_5.40.5_rf.n3_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_rf.n3_test))

##Node4

DryBean_TDA_KDE_5.40.5_n4_RfFit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.40.5.n4.vec, 
                 Importance = T,
                 method = 'rf', 
                 trControl = fitControl,
                 tuneGrid = rfGrid, preProc = c('center','scale'),                    
                 metric='Accuracy')
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
DryBean_TDA_KDE_5.40.5_n4_RfFit0
## Random Forest 
## 
## 1759 samples
##   16 predictor
##    4 classes: 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## Pre-processing: centered (16), scaled (16) 
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 1173, 1172, 1173 
## Resampling results across tuning parameters:
## 
##   mtry  Accuracy   Kappa    
##     50  0.8055790  0.6719209
##    100  0.8044394  0.6700029
##    150  0.8078523  0.6759512
##    200  0.8078523  0.6753621
##    250  0.8072855  0.6748779
##    300  0.8089890  0.6772184
##    350  0.8061459  0.6728635
##    400  0.8067157  0.6742178
##    450  0.8106965  0.6800007
##    500  0.8095569  0.6782018
##    550  0.8095579  0.6784758
##    600  0.8067166  0.6737180
##    650  0.8101257  0.6791846
##    700  0.8112653  0.6808640
##    750  0.8112643  0.6808839
##    800  0.8084212  0.6764512
##    850  0.8078543  0.6760808
##    900  0.8078494  0.6758194
##    950  0.8084251  0.6764376
##   1000  0.8089910  0.6773813
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 700.
DryBean_TDA_KDE_5.40.5_n4_RfFit0$resample
##    Accuracy     Kappa Resample
## 1 0.8259386 0.7044911    Fold1
## 2 0.8122867 0.6856285    Fold3
## 3 0.7955707 0.6524725    Fold2
ad_tda_kde_5.40.5_n4_rf_fit0_re<-DryBean_TDA_KDE_5.40.5_n4_RfFit0$resample[1]


summary(DryBean_TDA_KDE_5.40.5_n4_RfFit0)
##                 Length Class      Mode     
## call               5   -none-     call     
## type               1   -none-     character
## predicted       1759   factor     numeric  
## err.rate        2500   -none-     numeric  
## confusion         20   -none-     numeric  
## votes           7036   matrix     numeric  
## oob.times       1759   -none-     numeric  
## classes            4   -none-     character
## importance        16   -none-     numeric  
## importanceSD       0   -none-     NULL     
## localImportance    0   -none-     NULL     
## proximity          0   -none-     NULL     
## ntree              1   -none-     numeric  
## mtry               1   -none-     numeric  
## forest            14   -none-     list     
## y               1759   factor     numeric  
## test               0   -none-     NULL     
## inbag              0   -none-     NULL     
## xNames            16   -none-     character
## problemType        1   -none-     character
## tuneValue          1   data.frame list     
## obsLevels          4   -none-     character
## param              1   -none-     list
vip(DryBean_TDA_KDE_5.40.5_n4_RfFit0,25) + ggtitle("DryBean_TDA_KDE_5.40.5_n4_RfFit TDA-Assited RF")

# Predict outcome using DryBean_TDA_KDE_5.40.5_n4_RfFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.40.5_n4_RfFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
ad_tda_kde_5.40.5_n4_rf_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in confusionMatrix.default(data = pred0,
## as.factor(Dry_Bean_DatasetTest$Class)): Levels are not in the same order for
## reference and data. Refactoring data to match.
ad_tda_kde_5.40.5_n4_rf_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     0    0
##   DERMASON      352    145  482      999   539    21  329
##   HOROZ           0      0    0        0     1     0    0
##   SEKER          14      3    1       15     0   573    6
##   SIRA           30      8    6       49    38    14  455
## 
## Overall Statistics
##                                           
##                Accuracy : 0.4971          
##                  95% CI : (0.4816, 0.5125)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.3435          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000      0.0000          0.9398
## Specificity                  1.00000       1.00000      1.0000          0.3808
## Pos Pred Value                   NaN           NaN         NaN          0.3484
## Neg Pred Value               0.90294       0.96176      0.8801          0.9472
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.00000       0.00000      0.0000          0.2449
## Detection Prevalence         0.00000       0.00000      0.0000          0.7027
## Balanced Accuracy            0.50000       0.50000      0.5000          0.6603
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity             0.0017301       0.9424      0.5759
## Specificity             1.0000000       0.9888      0.9559
## Pos Pred Value          1.0000000       0.9363      0.7583
## Neg Pred Value          0.8585438       0.9899      0.9037
## Prevalence              0.1416667       0.1490      0.1936
## Detection Rate          0.0002451       0.1404      0.1115
## Detection Prevalence    0.0002451       0.1500      0.1471
## Balanced Accuracy       0.5008651       0.9656      0.7659
ad_tda_kde_5.40.5_n4_rf_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     0    0
##   DERMASON      352    145  482      999   539    21  329
##   HOROZ           0      0    0        0     1     0    0
##   SEKER          14      3    1       15     0   573    6
##   SIRA           30      8    6       49    38    14  455
## 
## Overall Statistics
##                                           
##                Accuracy : 0.4971          
##                  95% CI : (0.4816, 0.5125)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.3435          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000      0.0000          0.9398
## Specificity                  1.00000       1.00000      1.0000          0.3808
## Pos Pred Value                   NaN           NaN         NaN          0.3484
## Neg Pred Value               0.90294       0.96176      0.8801          0.9472
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.00000       0.00000      0.0000          0.2449
## Detection Prevalence         0.00000       0.00000      0.0000          0.7027
## Balanced Accuracy            0.50000       0.50000      0.5000          0.6603
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity             0.0017301       0.9424      0.5759
## Specificity             1.0000000       0.9888      0.9559
## Pos Pred Value          1.0000000       0.9363      0.7583
## Neg Pred Value          0.8585438       0.9899      0.9037
## Prevalence              0.1416667       0.1490      0.1936
## Detection Rate          0.0002451       0.1404      0.1115
## Detection Prevalence    0.0002451       0.1500      0.1471
## Balanced Accuracy       0.5008651       0.9656      0.7659
ad_tda_kde_5.40.5_n4_rf_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   4.970588e-01   3.434684e-01   4.816011e-01   5.125208e-01   2.605392e-01 
## AccuracyPValue  McnemarPValue 
##  7.312327e-228            NaN
ad_tda_kde_5.40.5_n4_rf_cf0_ov_acc<-ad_tda_kde_5.40.5_n4_rf_cf0$overall[1]
ad_tda_kde_5.40.5_n4_rf_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.000000000   1.0000000            NaN      0.9029412        NA
## Class: BOMBAY   0.000000000   1.0000000            NaN      0.9617647        NA
## Class: CALI     0.000000000   1.0000000            NaN      0.8801471        NA
## Class: DERMASON 0.939793039   0.3808419      0.3484479      0.9472383 0.3484479
## Class: HOROZ    0.001730104   1.0000000      1.0000000      0.8585438 1.0000000
## Class: SEKER    0.942434211   0.9887673      0.9362745      0.9899077 0.9362745
## Class: SIRA     0.575949367   0.9559271      0.7583333      0.9037356 0.7583333
##                      Recall          F1 Prevalence Detection Rate
## Class: BARBUNYA 0.000000000          NA 0.09705882    0.000000000
## Class: BOMBAY   0.000000000          NA 0.03823529    0.000000000
## Class: CALI     0.000000000          NA 0.11985294    0.000000000
## Class: DERMASON 0.939793039 0.508396947 0.26053922    0.244852941
## Class: HOROZ    0.001730104 0.003454231 0.14166667    0.000245098
## Class: SEKER    0.942434211 0.939344262 0.14901961    0.140441176
## Class: SIRA     0.575949367 0.654676259 0.19362745    0.111519608
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA          0.000000000         0.5000000
## Class: BOMBAY            0.000000000         0.5000000
## Class: CALI              0.000000000         0.5000000
## Class: DERMASON          0.702696078         0.6603175
## Class: HOROZ             0.000245098         0.5008651
## Class: SEKER             0.150000000         0.9656007
## Class: SIRA              0.147058824         0.7659382
ad_tda_kde_5.40.5_n4_rf_cf0_pre_rec_f1<-ad_tda_kde_5.40.5_n4_rf_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_kde_5.40.5_rf_n4_3_fold<-(db_rf_fit_re-ad_tda_kde_5.40.5_n4_rf_fit0_re)
diff_drybean_tda_kde_5.40.5_rf_n4_3_fold
##     Accuracy
## 1 0.09631513
## 2 0.11342939
## 3 0.11786965
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_rf.n4_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_rf_n4_3_fold),-0.01,0.01)
bst_tda_kde_5.40.5_rf.n4_3_fold
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_rf.n4_3_fold_odds.left<-bst_tda_kde_5.40.5_rf.n4_3_fold$probLeft/bst_tda_kde_5.40.5_rf.n4_3_fold$probRight
bst_tda_kde_5.40.5_rf.n4_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_rf.n4_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_rf_n4_3_fold),-0.01,0.01)
bsr_tda_kde_5.40.5_rf.n4_3_fold
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.009033333
## 
## $winRight
## [1] 0.9909667
# Bayesian Correlated Test

bct_tda_kde_5.40.5_rf.n4_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_rf_n4_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_rf.n4_3_fold
## $left
## [1] 0.002013533
## 
## $rope
## [1] 0.0008859466
## 
## $right
## [1] 0.9971005
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.40.5_rf_n4_3_fold,c(-0.01,0.01)))

### Test set diff
diff_drybean_tda_kde_5.40.5_rf.n4_test<-(db_rf_cf_ov_acc-ad_tda_kde_5.40.5_n4_rf_cf0_ov_acc)
diff_drybean_tda_kde_5.40.5_rf.n4_test
##  Accuracy 
## 0.4269608
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_rf.n4_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_rf.n4_test),-0.01,0.01)
bst_tda_kde_5.40.5_rf.n4_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 
#BayesFactor
#bf_tda_kde_5.40.5_rf.n4_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_rf_n4_3_fold))
#bf_tda_kde_5.40.5_rf.n4_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.40.5_rf_n4_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_kde_5.40.5_rf_n4_3_fold)
## t = 16.619, df = 2, p-value = 0.003601
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  0.08093188 0.13747758
## sample estimates:
## mean of x 
## 0.1092047
bst_tda_kde_5.40.5_rf.n4_test_odds.left<-bst_tda_kde_5.40.5_rf.n4_test$probLeft/bst_tda_kde_5.40.5_rf.n4_test$probRight
bst_tda_kde_5.40.5_rf.n4_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_rf.n4_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_rf.n4_test),-0.01,0.01)
bsr_tda_kde_5.40.5_rf.n4_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1593
## 
## $winRight
## [1] 0.8407
# Bayesian Correlated Test

bct_tda_kde_5.40.5_rf.n4_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_rf.n4_test),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_rf.n4_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_rf.n4_test))

#BayesFactor
#bf_tda_kde_5.40.5_rf.n4_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_rf.n4_test)) #bf_tda_kde_5.40.5_rf.n4_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_rf.n4_test))

##Node5

DryBean_TDA_KDE_5.40.5_n5_RfFit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.40.5.n5.vec, 
                 Importance = T,
                 method = 'rf', 
                 trControl = fitControl,
                 tuneGrid = rfGrid, preProc = c('center','scale'),                    
                 metric='Accuracy')
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range

## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry=  50 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 100 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 150 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 200 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 250 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 300 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 350 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 400 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 450 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 500 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 550 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 600 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 650 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 700 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 750 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 800 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 850 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 900 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 950 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry=1000 Error in randomForest.default(x, y, mtry = param$mtry, ...) : 
##   Can't have empty classes in y.
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
DryBean_TDA_KDE_5.40.5_n5_RfFit0
## Random Forest 
## 
## 774 samples
##  16 predictor
##   4 classes: 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## Pre-processing: centered (16), scaled (16) 
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 516, 517, 515 
## Resampling results across tuning parameters:
## 
##   mtry  Accuracy   Kappa    
##     50  0.7204175  0.5010763
##    100  0.7145960  0.4886543
##    150  0.7165339  0.4929159
##    200  0.7126354  0.4864193
##    250  0.7184870  0.4932110
##    300  0.7068289  0.4759614
##    350  0.7243010  0.5090073
##    400  0.7126580  0.4850380
##    450  0.7087594  0.4789768
##    500  0.7262390  0.5126513
##    550  0.7126655  0.4851590
##    600  0.7165490  0.4906555
##    650  0.7165339  0.4938777
##    700  0.7126580  0.4846906
##    750  0.7106974  0.4823407
##    800  0.7087518  0.4808751
##    850  0.7204175  0.5010197
##    900  0.7126580  0.4858585
##    950  0.7204175  0.4981472
##   1000  0.7204325  0.4997602
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 500.
DryBean_TDA_KDE_5.40.5_n5_RfFit0$resample
##    Accuracy     Kappa Resample
## 1 0.7131783 0.4894641    Fold1
## 2        NA        NA    Fold3
## 3 0.7392996 0.5358385    Fold2
ad_tda_kde_5.40.5_n5_rf_fit0_re<-DryBean_TDA_KDE_5.40.5_n5_RfFit0$resample[1]


summary(DryBean_TDA_KDE_5.40.5_n5_RfFit0)
##                 Length Class      Mode     
## call               5   -none-     call     
## type               1   -none-     character
## predicted        774   factor     numeric  
## err.rate        2500   -none-     numeric  
## confusion         20   -none-     numeric  
## votes           3096   matrix     numeric  
## oob.times        774   -none-     numeric  
## classes            4   -none-     character
## importance        16   -none-     numeric  
## importanceSD       0   -none-     NULL     
## localImportance    0   -none-     NULL     
## proximity          0   -none-     NULL     
## ntree              1   -none-     numeric  
## mtry               1   -none-     numeric  
## forest            14   -none-     list     
## y                774   factor     numeric  
## test               0   -none-     NULL     
## inbag              0   -none-     NULL     
## xNames            16   -none-     character
## problemType        1   -none-     character
## tuneValue          1   data.frame list     
## obsLevels          4   -none-     character
## param              1   -none-     list
vip(DryBean_TDA_KDE_5.40.5_n5_RfFit0,25) + ggtitle("DryBean_TDA_KDE_5.40.5_n5_RfFit TDA-Assited RF")

# Predict outcome using DryBean_TDA_KDE_5.40.5_n5_RfFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.40.5_n5_RfFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
ad_tda_kde_5.40.5_n5_rf_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in confusionMatrix.default(data = pred0,
## as.factor(Dry_Bean_DatasetTest$Class)): Levels are not in the same order for
## reference and data. Refactoring data to match.
ad_tda_kde_5.40.5_n5_rf_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     0    0
##   DERMASON      312     91  374     1028   503   131  568
##   HOROZ           0      0    0        0     0     0    0
##   SEKER          11      2    1        3     0   462    2
##   SIRA           73     63  114       32    75    15  220
## 
## Overall Statistics
##                                           
##                Accuracy : 0.4191          
##                  95% CI : (0.4039, 0.4344)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.238           
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000      0.0000          0.9671
## Specificity                  1.00000       1.00000      1.0000          0.3441
## Pos Pred Value                   NaN           NaN         NaN          0.3419
## Neg Pred Value               0.90294       0.96176      0.8801          0.9674
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.00000       0.00000      0.0000          0.2520
## Detection Prevalence         0.00000       0.00000      0.0000          0.7370
## Balanced Accuracy            0.50000       0.50000      0.5000          0.6556
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.0000       0.7599     0.27848
## Specificity                1.0000       0.9945     0.88693
## Pos Pred Value                NaN       0.9605     0.37162
## Neg Pred Value             0.8583       0.9594     0.83658
## Prevalence                 0.1417       0.1490     0.19363
## Detection Rate             0.0000       0.1132     0.05392
## Detection Prevalence       0.0000       0.1179     0.14510
## Balanced Accuracy          0.5000       0.8772     0.58271
ad_tda_kde_5.40.5_n5_rf_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     0    0
##   DERMASON      312     91  374     1028   503   131  568
##   HOROZ           0      0    0        0     0     0    0
##   SEKER          11      2    1        3     0   462    2
##   SIRA           73     63  114       32    75    15  220
## 
## Overall Statistics
##                                           
##                Accuracy : 0.4191          
##                  95% CI : (0.4039, 0.4344)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.238           
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000      0.0000          0.9671
## Specificity                  1.00000       1.00000      1.0000          0.3441
## Pos Pred Value                   NaN           NaN         NaN          0.3419
## Neg Pred Value               0.90294       0.96176      0.8801          0.9674
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.00000       0.00000      0.0000          0.2520
## Detection Prevalence         0.00000       0.00000      0.0000          0.7370
## Balanced Accuracy            0.50000       0.50000      0.5000          0.6556
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.0000       0.7599     0.27848
## Specificity                1.0000       0.9945     0.88693
## Pos Pred Value                NaN       0.9605     0.37162
## Neg Pred Value             0.8583       0.9594     0.83658
## Prevalence                 0.1417       0.1490     0.19363
## Detection Rate             0.0000       0.1132     0.05392
## Detection Prevalence       0.0000       0.1179     0.14510
## Balanced Accuracy          0.5000       0.8772     0.58271
ad_tda_kde_5.40.5_n5_rf_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   4.191176e-01   2.380040e-01   4.039174e-01   4.344338e-01   2.605392e-01 
## AccuracyPValue  McnemarPValue 
##  6.041558e-107            NaN
ad_tda_kde_5.40.5_n5_rf_cf0_ov_acc<-ad_tda_kde_5.40.5_n5_rf_cf0$overall[1]
ad_tda_kde_5.40.5_n5_rf_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.0000000   1.0000000            NaN      0.9029412        NA
## Class: BOMBAY     0.0000000   1.0000000            NaN      0.9617647        NA
## Class: CALI       0.0000000   1.0000000            NaN      0.8801471        NA
## Class: DERMASON   0.9670743   0.3440504      0.3418690      0.9673812 0.3418690
## Class: HOROZ      0.0000000   1.0000000            NaN      0.8583333        NA
## Class: SEKER      0.7598684   0.9945276      0.9604990      0.9594332 0.9604990
## Class: SIRA       0.2784810   0.8869301      0.3716216      0.8365826 0.3716216
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.0000000        NA 0.09705882     0.00000000
## Class: BOMBAY   0.0000000        NA 0.03823529     0.00000000
## Class: CALI     0.0000000        NA 0.11985294     0.00000000
## Class: DERMASON 0.9670743 0.5051597 0.26053922     0.25196078
## Class: HOROZ    0.0000000        NA 0.14166667     0.00000000
## Class: SEKER    0.7598684 0.8484848 0.14901961     0.11323529
## Class: SIRA     0.2784810 0.3183792 0.19362745     0.05392157
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA            0.0000000         0.5000000
## Class: BOMBAY              0.0000000         0.5000000
## Class: CALI                0.0000000         0.5000000
## Class: DERMASON            0.7370098         0.6555623
## Class: HOROZ               0.0000000         0.5000000
## Class: SEKER               0.1178922         0.8771980
## Class: SIRA                0.1450980         0.5827056
ad_tda_kde_5.40.5_n5_rf_cf0_pre_rec_f1<-ad_tda_kde_5.40.5_n5_rf_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_kde_5.40.5_rf_n5_3_fold<-(db_rf_fit_re-ad_tda_kde_5.40.5_n5_rf_fit0_re)
diff_drybean_tda_kde_5.40.5_rf_n5_3_fold
##    Accuracy
## 1 0.2090754
## 2        NA
## 3 0.1741407
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_rf.n5_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_rf_n5_3_fold),-0.01,0.01)
bst_tda_kde_5.40.5_rf.n5_3_fold
## $probLeft
## [1] NA
## 
## $probRope
## [1] NA
## 
## $probRight
## [1] NA
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_rf.n5_3_fold_odds.left<-bst_tda_kde_5.40.5_rf.n5_3_fold$probLeft/bst_tda_kde_5.40.5_rf.n5_3_fold$probRight
bst_tda_kde_5.40.5_rf.n5_3_fold_odds.left
## [1] NA
# Bayesian Signed Rank Test

#bsr_tda_kde_5.40.5_rf.n5_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_rf_n5_3_fold),-0.01,0.01)
#bsr_tda_kde_5.40.5_rf.n5_3_fold

# Bayesian Correlated Test

#bct_tda_kde_5.40.5_rf.n5_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_rf_n5_3_fold),0.1,-0.01,0.01)
#bct_tda_kde_5.40.5_rf.n5_3_fold

# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_rf_n5_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_kde_5.40.5_rf.n5_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_rf_n5_3_fold))
#bf_tda_kde_5.40.5_rf.n5_3_fold

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_rf_n5_3_fold))


### Test set diff
diff_drybean_tda_kde_5.40.5_rf.n5_test<-(db_rf_cf_ov_acc-ad_tda_kde_5.40.5_n5_rf_cf0_ov_acc)
diff_drybean_tda_kde_5.40.5_rf.n5_test
## Accuracy 
## 0.504902
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_rf.n5_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_rf.n5_test),-0.01,0.01)
bst_tda_kde_5.40.5_rf.n5_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_rf.n5_test_odds.left<-bst_tda_kde_5.40.5_rf.n5_test$probLeft/bst_tda_kde_5.40.5_rf.n5_test$probRight
bst_tda_kde_5.40.5_rf.n5_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_rf.n5_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_rf.n5_test),-0.01,0.01)
bsr_tda_kde_5.40.5_rf.n5_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1557
## 
## $winRight
## [1] 0.8443
# Bayesian Correlated Test

bct_tda_kde_5.40.5_rf.n5_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_rf.n5_test),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_rf.n5_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_rf.n5_test))

#BayesFactor
#bf_tda_kde_5.40.5_rf.n5_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_rf.n5_test)) #bf_tda_kde_5.40.5_rf.n5_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_rf.n5_test))

##Non-TDA-Assisted

svmGrid<-expand.grid(sigma = c(0.1, 1, 10), C = (1:5*0.25))

#Support Vector Machine-Radial Basis 
dryBeanSvmFit <- train(as.factor(Class) ~ ., data = Dry_Bean_DatasetTrain, 
                   Importance = T,
                   method = 'svmRadial', 
                           trControl = fitControl,
           tuneGrid = svmGrid, preProc = c('center','scale'),
                           metric='Accuracy')

dryBeanSvmFit
## Support Vector Machines with Radial Basis Function Kernel 
## 
## 9531 samples
##   16 predictor
##    7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## Pre-processing: centered (16), scaled (16) 
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 6354, 6353, 6355 
## Resampling results across tuning parameters:
## 
##   sigma  C     Accuracy   Kappa    
##    0.1   0.25  0.9277092  0.9125640
##    0.1   0.50  0.9297028  0.9149672
##    0.1   0.75  0.9297026  0.9149615
##    0.1   1.00  0.9299124  0.9152168
##    0.1   1.25  0.9311713  0.9167360
##    1.0   0.25  0.8984359  0.8770496
##    1.0   0.50  0.9128105  0.8945247
##    1.0   0.75  0.9164825  0.8989732
##    1.0   1.00  0.9198400  0.9030263
##    1.0   1.25  0.9198402  0.9030237
##   10.0   0.25  0.3500151  0.1314073
##   10.0   0.50  0.4503195  0.2748202
##   10.0   0.75  0.5300599  0.3873530
##   10.0   1.00  0.6081214  0.4957386
##   10.0   1.25  0.6314138  0.5279090
## 
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.1 and C = 1.25.
dryBeanSvmFit$resample
##    Accuracy    Kappa Resample
## 1 0.9285489 0.913598    Fold1
## 2 0.9294710 0.914659    Fold3
## 3 0.9354940 0.921951    Fold2
db_svm_fit_re<-dryBeanSvmFit$resample[1]

summary(dryBeanSvmFit)
## Length  Class   Mode 
##      1   ksvm     S4
#vip(dryBeanSvmFit, 25) + ggtitle("non-TDA-Assited Svm")

# Predict outcome using model from training data based on testing data
predictions <- predict(dryBeanSvmFit, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
db_svm_cf<-confusionMatrix(data=predictions, as.factor(Dry_Bean_DatasetTest$Class))
db_svm_cf
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      346      0   11        0     2     3    4
##   BOMBAY          0    156    0        0     0     0    0
##   CALI           29      0  462        0    10     0    0
##   DERMASON        0      0    0      993     4    12   71
##   HOROZ           2      0    8        2   558     1   11
##   SEKER           4      0    1       11     0   576   11
##   SIRA           15      0    7       57     4    16  693
## 
## Overall Statistics
##                                           
##                Accuracy : 0.9275          
##                  95% CI : (0.9191, 0.9352)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.9122          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.87374       1.00000      0.9448          0.9341
## Specificity                  0.99457       1.00000      0.9891          0.9712
## Pos Pred Value               0.94536       1.00000      0.9222          0.9194
## Neg Pred Value               0.98654       1.00000      0.9925          0.9767
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.08480       0.03824      0.1132          0.2434
## Detection Prevalence         0.08971       0.03824      0.1228          0.2647
## Balanced Accuracy            0.93415       1.00000      0.9670          0.9527
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9654       0.9474      0.8772
## Specificity                0.9931       0.9922      0.9699
## Pos Pred Value             0.9588       0.9552      0.8750
## Neg Pred Value             0.9943       0.9908      0.9705
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1368       0.1412      0.1699
## Detection Prevalence       0.1426       0.1478      0.1941
## Balanced Accuracy          0.9793       0.9698      0.9236
db_svm_cf$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.9274510      0.9122032      0.9190588      0.9352246      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
db_svm_cf_ov_acc<-db_svm_cf$overall[1]
db_svm_cf$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.8737374   0.9945711      0.9453552      0.9865374 0.9453552
## Class: BOMBAY     1.0000000   1.0000000      1.0000000      1.0000000 1.0000000
## Class: CALI       0.9447853   0.9891395      0.9221557      0.9924560 0.9221557
## Class: DERMASON   0.9341486   0.9711634      0.9194444      0.9766667 0.9194444
## Class: HOROZ      0.9653979   0.9931468      0.9587629      0.9942824 0.9587629
## Class: SEKER      0.9473684   0.9922235      0.9552239      0.9907967 0.9552239
## Class: SIRA       0.8772152   0.9699088      0.8750000      0.9704988 0.8750000
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.8737374 0.9081365 0.09705882     0.08480392
## Class: BOMBAY   1.0000000 1.0000000 0.03823529     0.03823529
## Class: CALI     0.9447853 0.9333333 0.11985294     0.11323529
## Class: DERMASON 0.9341486 0.9267382 0.26053922     0.24338235
## Class: HOROZ    0.9653979 0.9620690 0.14166667     0.13676471
## Class: SEKER    0.9473684 0.9512799 0.14901961     0.14117647
## Class: SIRA     0.8772152 0.8761062 0.19362745     0.16985294
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA           0.08970588         0.9341542
## Class: BOMBAY             0.03823529         1.0000000
## Class: CALI               0.12279412         0.9669624
## Class: DERMASON           0.26470588         0.9526560
## Class: HOROZ              0.14264706         0.9792723
## Class: SEKER              0.14779412         0.9697960
## Class: SIRA               0.19411765         0.9235620
db_svm_cf_pr_rec_f1<-db_svm_cf$byClass[5:7]

##With TDA PCA filter 5 intervals, 50% overlap, 5 bins 
##Node1

DryBean_TDA_PC_5.40.5_n1_SvmFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.40.5.n1.vec, 
                    Importance = T,
                    method = 'svmRadial', 
                  trControl = fitControl,
                          tuneGrid = svmGrid, preProc = c('center','scale'),
                          metric='Accuracy')

DryBean_TDA_PC_5.40.5_n1_SvmFit0
## Support Vector Machines with Radial Basis Function Kernel 
## 
## 6835 samples
##   16 predictor
##    6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## Pre-processing: centered (16), scaled (16) 
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 4557, 4555, 4558 
## Resampling results across tuning parameters:
## 
##   sigma  C     Accuracy   Kappa       
##    0.1   0.25  0.9106090  0.8525204728
##    0.1   0.50  0.9126561  0.8557647610
##    0.1   0.75  0.9135333  0.8572004449
##    0.1   1.00  0.9139729  0.8579434295
##    0.1   1.25  0.9145579  0.8589339182
##    1.0   0.25  0.8645229  0.7683515269
##    1.0   0.50  0.8835420  0.8038961152
##    1.0   0.75  0.8892479  0.8148415388
##    1.0   1.00  0.8930512  0.8217918544
##    1.0   1.25  0.8931978  0.8223875190
##   10.0   0.25  0.5149965  0.0003681942
##   10.0   0.50  0.5265545  0.0292875825
##   10.0   0.75  0.5536213  0.0959021520
##   10.0   1.00  0.5855131  0.1728353181
##   10.0   1.25  0.6092155  0.2297499248
## 
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.1 and C = 1.25.
DryBean_TDA_PC_5.40.5_n1_SvmFit0$resample
##    Accuracy     Kappa Resample
## 1 0.9117647 0.8550654    Fold1
## 2 0.9174352 0.8632422    Fold3
## 3 0.9144737 0.8584941    Fold2
db_tda_pc_5.40.5_n1_svm_fit_re<-DryBean_TDA_PC_5.40.5_n1_SvmFit0 $resample[1]

summary(DryBean_TDA_PC_5.40.5_n1_SvmFit0)
## Length  Class   Mode 
##      1   ksvm     S4
#vip(DryBean_TDA_PC_5.40.5_n1_SvmFit0,25) + ggtitle("Adult_TDA_PCA_5.40.5_n1_SvmFit TDA-Assited Svm")

# Predict outcome using DryBean_TDA_PC_5.40.5_n1_SvmFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.40.5_n1_SvmFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.40.5_n1_db_svm_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length

## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
db_tda_pc_5.40.5_n1_db_svm_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        2      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     0    0
##   DERMASON      331    156  481     1020   578    13  160
##   HOROZ           0      0    0        0     0     0    0
##   SEKER          58      0    6       12     0   584   59
##   SIRA            5      0    2       31     0    11  571
## 
## Overall Statistics
##                                          
##                Accuracy : 0.5336         
##                  95% CI : (0.5181, 0.549)
##     No Information Rate : 0.2605         
##     P-Value [Acc > NIR] : < 2.2e-16      
##                                          
##                   Kappa : 0.3938         
##                                          
##  Mcnemar's Test P-Value : NA             
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                0.0050505       0.00000      0.0000          0.9595
## Specificity                1.0000000       1.00000      1.0000          0.4302
## Pos Pred Value             1.0000000           NaN         NaN          0.3724
## Neg Pred Value             0.9033840       0.96176      0.8801          0.9679
## Prevalence                 0.0970588       0.03824      0.1199          0.2605
## Detection Rate             0.0004902       0.00000      0.0000          0.2500
## Detection Prevalence       0.0004902       0.00000      0.0000          0.6713
## Balanced Accuracy          0.5025253       0.50000      0.5000          0.6949
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.0000       0.9605      0.7228
## Specificity                1.0000       0.9611      0.9851
## Pos Pred Value                NaN       0.8122      0.9210
## Neg Pred Value             0.8583       0.9929      0.9367
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.0000       0.1431      0.1400
## Detection Prevalence       0.0000       0.1762      0.1520
## Balanced Accuracy          0.5000       0.9608      0.8539
db_tda_pc_5.40.5_n1_db_svm_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        2      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     0    0
##   DERMASON      331    156  481     1020   578    13  160
##   HOROZ           0      0    0        0     0     0    0
##   SEKER          58      0    6       12     0   584   59
##   SIRA            5      0    2       31     0    11  571
## 
## Overall Statistics
##                                          
##                Accuracy : 0.5336         
##                  95% CI : (0.5181, 0.549)
##     No Information Rate : 0.2605         
##     P-Value [Acc > NIR] : < 2.2e-16      
##                                          
##                   Kappa : 0.3938         
##                                          
##  Mcnemar's Test P-Value : NA             
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                0.0050505       0.00000      0.0000          0.9595
## Specificity                1.0000000       1.00000      1.0000          0.4302
## Pos Pred Value             1.0000000           NaN         NaN          0.3724
## Neg Pred Value             0.9033840       0.96176      0.8801          0.9679
## Prevalence                 0.0970588       0.03824      0.1199          0.2605
## Detection Rate             0.0004902       0.00000      0.0000          0.2500
## Detection Prevalence       0.0004902       0.00000      0.0000          0.6713
## Balanced Accuracy          0.5025253       0.50000      0.5000          0.6949
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.0000       0.9605      0.7228
## Specificity                1.0000       0.9611      0.9851
## Pos Pred Value                NaN       0.8122      0.9210
## Neg Pred Value             0.8583       0.9929      0.9367
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.0000       0.1431      0.1400
## Detection Prevalence       0.0000       0.1762      0.1520
## Balanced Accuracy          0.5000       0.9608      0.8539
db_tda_pc_5.40.5_n1_db_svm_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.335784e-01   3.937550e-01   5.181289e-01   5.489799e-01   2.605392e-01 
## AccuracyPValue  McnemarPValue 
##  2.302491e-299            NaN
db_tda_pc_5.40.5_n1_db_svm_cf0_ov_acc<-db_tda_pc_5.40.5_n1_db_svm_cf0$overall[1]
db_tda_pc_5.40.5_n1_db_svm_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.005050505   1.0000000      1.0000000      0.9033840 1.0000000
## Class: BOMBAY   0.000000000   1.0000000            NaN      0.9617647        NA
## Class: CALI     0.000000000   1.0000000            NaN      0.8801471        NA
## Class: DERMASON 0.959548448   0.4302287      0.3723987      0.9679344 0.3723987
## Class: HOROZ    0.000000000   1.0000000            NaN      0.8583333        NA
## Class: SEKER    0.960526316   0.9611175      0.8122392      0.9928593 0.8122392
## Class: SIRA     0.722784810   0.9851064      0.9209677      0.9367052 0.9209677
##                      Recall         F1 Prevalence Detection Rate
## Class: BARBUNYA 0.005050505 0.01005025 0.09705882   0.0004901961
## Class: BOMBAY   0.000000000         NA 0.03823529   0.0000000000
## Class: CALI     0.000000000         NA 0.11985294   0.0000000000
## Class: DERMASON 0.959548448 0.53655971 0.26053922   0.2500000000
## Class: HOROZ    0.000000000         NA 0.14166667   0.0000000000
## Class: SEKER    0.960526316 0.88018086 0.14901961   0.1431372549
## Class: SIRA     0.722784810 0.80992908 0.19362745   0.1399509804
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA         0.0004901961         0.5025253
## Class: BOMBAY           0.0000000000         0.5000000
## Class: CALI             0.0000000000         0.5000000
## Class: DERMASON         0.6713235294         0.6948886
## Class: HOROZ            0.0000000000         0.5000000
## Class: SEKER            0.1762254902         0.9608219
## Class: SIRA             0.1519607843         0.8539456
db_tda_pc_5.40.5_n1_db_svm_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n1_db_svm_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_pca_5.40.5_svm_n1_3_fold<-(db_svm_fit_re - db_tda_pc_5.40.5_n1_svm_fit_re)
diff_drybean_tda_pca_5.40.5_svm_n1_3_fold
##     Accuracy
## 1 0.01678424
## 2 0.01203581
## 3 0.02102034
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_svm.n1_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_svm_n1_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_svm.n1_3_fold
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_svm.n1_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_svm.n1_3_fold$probLeft/bst_dbf_db_tda_pca_5.40.5_svm.n1_3_fold$probRight
bst_dbf_db_tda_pca_5.40.5_svm.n1_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_svm.n1_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_svm_n1_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_svm.n1_3_fold
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.09173333
## 
## $winRight
## [1] 0.9082667
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_svm.n1_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_svm_n1_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_svm.n1_3_fold
## $left
## [1] 0.006220449
## 
## $rope
## [1] 0.07278914
## 
## $right
## [1] 0.9209904
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.40.5_svm_n1_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_rf.n1_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_svm_n1_3_fold))
#bf_tda_pca_5.40.5_rf.n1_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.40.5_svm_n1_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_pca_5.40.5_svm_n1_3_fold)
## t = 6.4021, df = 2, p-value = 0.02354
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  0.005448016 0.027778909
## sample estimates:
##  mean of x 
## 0.01661346
### Test set diff
diff_drybean_tda_pca_5.40.5_svm.n1_test<-(db_svm_cf_ov_acc - db_tda_pc_5.40.5_n1_db_svm_cf0_ov_acc)
diff_drybean_tda_pca_5.40.5_svm.n1_test
##  Accuracy 
## 0.3938725
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_svm.n1_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_svm.n1_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_svm.n1_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_svm.n1_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_svm.n1_test$probLeft/bst_dbf_db_tda_pca_5.40.5_svm.n1_test$probRight
bst_dbf_db_tda_pca_5.40.5_svm.n1_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_svm.n1_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_svm.n1_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_svm.n1_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1586667
## 
## $winRight
## [1] 0.8413333
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_svm.n1_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_svm.n1_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_svm.n1_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_svm.n1_test)))

#BayesFactor
#bf_tda_pca_5.40.5_svm.n1_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_svm.n1_test)) #bf_tda_pca_5.40.5_svm.n1_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_svm.n1_test))

##With TDA PCA filter 5 intervals, 50% overlap, 5 bins 
##Node2

DryBean_TDA_PC_5.40.5_n2_SvmFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.40.5.n2.vec, 
                    Importance = T,
                    method = 'svmRadial', 
                  trControl = fitControl,
                          tuneGrid = svmGrid, preProc = c('center','scale'),
                          metric='Accuracy')

DryBean_TDA_PC_5.40.5_n2_SvmFit0
## Support Vector Machines with Radial Basis Function Kernel 
## 
## 8024 samples
##   16 predictor
##    6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## Pre-processing: centered (16), scaled (16) 
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 5349, 5350, 5349 
## Resampling results across tuning parameters:
## 
##   sigma  C     Accuracy   Kappa      
##    0.1   0.25  0.8904552  0.856994446
##    0.1   0.50  0.8921997  0.859308951
##    0.1   0.75  0.8933215  0.860790502
##    0.1   1.00  0.8930722  0.860511409
##    0.1   1.25  0.8936952  0.861363079
##    1.0   0.25  0.8610430  0.818387419
##    1.0   0.50  0.8769952  0.839561228
##    1.0   0.75  0.8812320  0.845157612
##    1.0   1.00  0.8822290  0.846489251
##    1.0   1.25  0.8818551  0.846034487
##   10.0   0.25  0.3311317  0.005034164
##   10.0   0.50  0.3885836  0.094429567
##   10.0   0.75  0.4525166  0.195280745
##   10.0   1.00  0.4922727  0.257703232
##   10.0   1.25  0.5107175  0.287513996
## 
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.1 and C = 1.25.
DryBean_TDA_PC_5.40.5_n2_SvmFit0$resample
##    Accuracy     Kappa Resample
## 1 0.8968224 0.8653942    Fold1
## 2 0.8800000 0.8433578    Fold3
## 3 0.9042633 0.8753372    Fold2
db_tda_pc_5.40.5_n2_svm_fit_re<-DryBean_TDA_PC_5.40.5_n2_SvmFit0 $resample[1]

summary(DryBean_TDA_PC_5.40.5_n2_SvmFit0)
## Length  Class   Mode 
##      1   ksvm     S4
#vip(DryBean_TDA_PC_5.40.5_n2_SvmFit0,25) + ggtitle("Adult_TDA_PCA_5.40.5_n2_SvmFit TDA-Assited Svm")

# Predict outcome using DryBean_TDA_PC_5.40.5_n2_SvmFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.40.5_n2_SvmFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.40.5_n2_db_svm_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length

## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
db_tda_pc_5.40.5_n2_db_svm_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      361     12   90        0     4    15    3
##   BOMBAY          0      0    0        0     0     0    0
##   CALI           20      0  369        0     6     0    0
##   DERMASON        0    144    0     1009     6    67   76
##   HOROZ           2      0   23        2   558     0   10
##   SEKER           3      0    1        7     0   511   10
##   SIRA           10      0    6       45     4    15  691
## 
## Overall Statistics
##                                           
##                Accuracy : 0.8576          
##                  95% CI : (0.8465, 0.8682)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.8257          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.91162       0.00000     0.75460          0.9492
## Specificity                  0.96634       1.00000     0.99276          0.9029
## Pos Pred Value               0.74433           NaN     0.93418          0.7750
## Neg Pred Value               0.99026       0.96176     0.96744          0.9806
## Prevalence                   0.09706       0.03824     0.11985          0.2605
## Detection Rate               0.08848       0.00000     0.09044          0.2473
## Detection Prevalence         0.11887       0.00000     0.09681          0.3191
## Balanced Accuracy            0.93898       0.50000     0.87368          0.9260
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9654       0.8405      0.8747
## Specificity                0.9894       0.9940      0.9757
## Pos Pred Value             0.9378       0.9605      0.8962
## Neg Pred Value             0.9943       0.9727      0.9701
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1368       0.1252      0.1694
## Detection Prevalence       0.1458       0.1304      0.1890
## Balanced Accuracy          0.9774       0.9172      0.9252
db_tda_pc_5.40.5_n2_db_svm_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      361     12   90        0     4    15    3
##   BOMBAY          0      0    0        0     0     0    0
##   CALI           20      0  369        0     6     0    0
##   DERMASON        0    144    0     1009     6    67   76
##   HOROZ           2      0   23        2   558     0   10
##   SEKER           3      0    1        7     0   511   10
##   SIRA           10      0    6       45     4    15  691
## 
## Overall Statistics
##                                           
##                Accuracy : 0.8576          
##                  95% CI : (0.8465, 0.8682)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.8257          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.91162       0.00000     0.75460          0.9492
## Specificity                  0.96634       1.00000     0.99276          0.9029
## Pos Pred Value               0.74433           NaN     0.93418          0.7750
## Neg Pred Value               0.99026       0.96176     0.96744          0.9806
## Prevalence                   0.09706       0.03824     0.11985          0.2605
## Detection Rate               0.08848       0.00000     0.09044          0.2473
## Detection Prevalence         0.11887       0.00000     0.09681          0.3191
## Balanced Accuracy            0.93898       0.50000     0.87368          0.9260
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9654       0.8405      0.8747
## Specificity                0.9894       0.9940      0.9757
## Pos Pred Value             0.9378       0.9605      0.8962
## Neg Pred Value             0.9943       0.9727      0.9701
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1368       0.1252      0.1694
## Detection Prevalence       0.1458       0.1304      0.1890
## Balanced Accuracy          0.9774       0.9172      0.9252
db_tda_pc_5.40.5_n2_db_svm_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.8575980      0.8257090      0.8464965      0.8681847      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
db_tda_pc_5.40.5_n2_db_svm_cf0_ov_acc<-db_tda_pc_5.40.5_n2_db_svm_cf0$overall[1]
db_tda_pc_5.40.5_n2_db_svm_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.9116162   0.9663409      0.7443299      0.9902643 0.7443299
## Class: BOMBAY     0.0000000   1.0000000            NaN      0.9617647        NA
## Class: CALI       0.7546012   0.9927597      0.9341772      0.9674355 0.9341772
## Class: DERMASON   0.9492004   0.9028837      0.7749616      0.9805616 0.7749616
## Class: HOROZ      0.9653979   0.9894346      0.9378151      0.9942611 0.9378151
## Class: SEKER      0.8404605   0.9939516      0.9605263      0.9726607 0.9605263
## Class: SIRA       0.8746835   0.9756839      0.8962387      0.9700816 0.8962387
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.9116162 0.8195233 0.09705882     0.08848039
## Class: BOMBAY   0.0000000        NA 0.03823529     0.00000000
## Class: CALI     0.7546012 0.8348416 0.11985294     0.09044118
## Class: DERMASON 0.9492004 0.8532770 0.26053922     0.24730392
## Class: HOROZ    0.9653979 0.9514066 0.14166667     0.13676471
## Class: SEKER    0.8404605 0.8964912 0.14901961     0.12524510
## Class: SIRA     0.8746835 0.8853299 0.19362745     0.16936275
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA           0.11887255         0.9389785
## Class: BOMBAY             0.00000000         0.5000000
## Class: CALI               0.09681373         0.8736805
## Class: DERMASON           0.31911765         0.9260420
## Class: HOROZ              0.14583333         0.9774163
## Class: SEKER              0.13039216         0.9172061
## Class: SIRA               0.18897059         0.9251837
db_tda_pc_5.40.5_n2_db_svm_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n2_db_svm_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_pca_5.40.5_svm_n2_3_fold<-(db_svm_fit_re - db_tda_pc_5.40.5_n2_svm_fit_re)
diff_drybean_tda_pca_5.40.5_svm_n2_3_fold
##     Accuracy
## 1 0.03172652
## 2 0.04947103
## 3 0.03123075
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_svm.n2_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_svm_n2_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_svm.n2_3_fold
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_svm.n2_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_svm.n2_3_fold$probLeft/bst_dbf_db_tda_pca_5.40.5_svm.n2_3_fold$probRight
bst_dbf_db_tda_pca_5.40.5_svm.n2_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_svm.n2_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_svm_n2_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_svm.n2_3_fold
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.0094
## 
## $winRight
## [1] 0.9906
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_svm.n2_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_svm_n2_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_svm.n2_3_fold
## $left
## [1] 0.01031659
## 
## $rope
## [1] 0.01872413
## 
## $right
## [1] 0.9709593
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.40.5_svm_n2_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_rf.n2_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_svm_n2_3_fold))
#bf_tda_pca_5.40.5_rf.n2_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.40.5_svm_n2_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_pca_5.40.5_svm_n2_3_fold)
## t = 6.2469, df = 2, p-value = 0.02468
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  0.01166373 0.06328846
## sample estimates:
## mean of x 
## 0.0374761
### Test set diff
diff_drybean_tda_pca_5.40.5_svm.n2_test<-(db_svm_cf_ov_acc - db_tda_pc_5.40.5_n2_db_svm_cf0_ov_acc)
diff_drybean_tda_pca_5.40.5_svm.n2_test
##   Accuracy 
## 0.06985294
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_svm.n2_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_svm.n2_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_svm.n2_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_svm.n2_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_svm.n2_test$probLeft/bst_dbf_db_tda_pca_5.40.5_svm.n2_test$probRight
bst_dbf_db_tda_pca_5.40.5_svm.n2_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_svm.n2_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_svm.n2_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_svm.n2_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1592667
## 
## $winRight
## [1] 0.8407333
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_svm.n2_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_svm.n2_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_svm.n2_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_svm.n2_test)))

#BayesFactor
#bf_tda_pca_5.40.5_svm.n2_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_svm.n2_test)) #bf_tda_pca_5.40.5_svm.n2_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_svm.n2_test))

##Node3

DryBean_TDA_PC_5.40.5_n3_SvmFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.40.5.n3.vec, 
                    Importance = T,
                    method = 'svmRadial', 
                  trControl = fitControl,
                          tuneGrid = svmGrid, preProc = c('center','scale'),
                          metric='Accuracy')

DryBean_TDA_PC_5.40.5_n3_SvmFit0
## Support Vector Machines with Radial Basis Function Kernel 
## 
## 5008 samples
##   16 predictor
##    7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## Pre-processing: centered (16), scaled (16) 
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 3339, 3339, 3338 
## Resampling results across tuning parameters:
## 
##   sigma  C     Accuracy   Kappa     
##    0.1   0.25  0.8009705  0.75003112
##    0.1   0.50  0.8021685  0.75163104
##    0.1   0.75  0.8035661  0.75355578
##    0.1   1.00  0.8031666  0.75308351
##    0.1   1.25  0.8039649  0.75393389
##    1.0   0.25  0.7722167  0.71133345
##    1.0   0.50  0.7861945  0.73084032
##    1.0   0.75  0.7867932  0.73134489
##    1.0   1.00  0.7871928  0.73192630
##    1.0   1.25  0.7875922  0.73256122
##   10.0   0.25  0.2518473  0.00000000
##   10.0   0.50  0.2546430  0.00412182
##   10.0   0.75  0.3013690  0.06863355
##   10.0   1.00  0.4010095  0.20394619
##   10.0   1.25  0.4253706  0.23783452
## 
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.1 and C = 1.25.
DryBean_TDA_PC_5.40.5_n3_SvmFit0$resample
##    Accuracy     Kappa Resample
## 1 0.9478730 0.9245344    Fold1
## 2 0.5479042 0.4586663    Fold3
## 3 0.9161174 0.8786009    Fold2
db_tda_pc_5.40.5_n3_svm_fit_re<-DryBean_TDA_PC_5.40.5_n3_SvmFit0 $resample[1]

summary(DryBean_TDA_PC_5.40.5_n3_SvmFit0)
## Length  Class   Mode 
##      1   ksvm     S4
#vip(DryBean_TDA_PC_5.40.5_n3_SvmFit0,25) + ggtitle("Adult_TDA_PCA_5.40.5_n3_SvmFit TDA-Assited Svm")

# Predict outcome using DryBean_TDA_PC_5.40.5_n3_SvmFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.40.5_n3_SvmFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.40.5_n3_db_svm_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
db_tda_pc_5.40.5_n3_db_svm_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      357      0   12        4     1    23  128
##   BOMBAY          0     61    0        0     0     0    0
##   CALI           29      0  465        0     9     0    1
##   DERMASON        0      0    0        0     0     0    0
##   HOROZ           7     95    8     1059   566   581  340
##   SEKER           0      0    0        0     0     1    0
##   SIRA            3      0    4        0     2     3  321
## 
## Overall Statistics
##                                           
##                Accuracy : 0.4341          
##                  95% CI : (0.4188, 0.4494)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.345           
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.90152       0.39103      0.9509          0.0000
## Specificity                  0.95440       1.00000      0.9891          1.0000
## Pos Pred Value               0.68000       1.00000      0.9226             NaN
## Neg Pred Value               0.98903       0.97636      0.9933          0.7395
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.08750       0.01495      0.1140          0.0000
## Detection Prevalence         0.12868       0.01495      0.1235          0.0000
## Balanced Accuracy            0.92796       0.69551      0.9700          0.5000
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9792    0.0016447     0.40633
## Specificity                0.4032    1.0000000     0.99635
## Pos Pred Value             0.2131    1.0000000     0.96396
## Neg Pred Value             0.9916    0.8511890     0.87483
## Prevalence                 0.1417    0.1490196     0.19363
## Detection Rate             0.1387    0.0002451     0.07868
## Detection Prevalence       0.6510    0.0002451     0.08162
## Balanced Accuracy          0.6912    0.5008224     0.70134
db_tda_pc_5.40.5_n3_db_svm_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      357      0   12        4     1    23  128
##   BOMBAY          0     61    0        0     0     0    0
##   CALI           29      0  465        0     9     0    1
##   DERMASON        0      0    0        0     0     0    0
##   HOROZ           7     95    8     1059   566   581  340
##   SEKER           0      0    0        0     0     1    0
##   SIRA            3      0    4        0     2     3  321
## 
## Overall Statistics
##                                           
##                Accuracy : 0.4341          
##                  95% CI : (0.4188, 0.4494)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.345           
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.90152       0.39103      0.9509          0.0000
## Specificity                  0.95440       1.00000      0.9891          1.0000
## Pos Pred Value               0.68000       1.00000      0.9226             NaN
## Neg Pred Value               0.98903       0.97636      0.9933          0.7395
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.08750       0.01495      0.1140          0.0000
## Detection Prevalence         0.12868       0.01495      0.1235          0.0000
## Balanced Accuracy            0.92796       0.69551      0.9700          0.5000
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9792    0.0016447     0.40633
## Specificity                0.4032    1.0000000     0.99635
## Pos Pred Value             0.2131    1.0000000     0.96396
## Neg Pred Value             0.9916    0.8511890     0.87483
## Prevalence                 0.1417    0.1490196     0.19363
## Detection Rate             0.1387    0.0002451     0.07868
## Detection Prevalence       0.6510    0.0002451     0.08162
## Balanced Accuracy          0.6912    0.5008224     0.70134
db_tda_pc_5.40.5_n3_db_svm_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   4.340686e-01   3.450411e-01   4.187896e-01   4.494421e-01   2.605392e-01 
## AccuracyPValue  McnemarPValue 
##  9.033230e-127            NaN
db_tda_pc_5.40.5_n3_db_svm_cf0_ov_acc<-db_tda_pc_5.40.5_n3_db_svm_cf0$overall[1]
db_tda_pc_5.40.5_n3_db_svm_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.901515152   0.9543974      0.6800000      0.9890295 0.6800000
## Class: BOMBAY   0.391025641   1.0000000      1.0000000      0.9763623 1.0000000
## Class: CALI     0.950920245   0.9891395      0.9226190      0.9932886 0.9226190
## Class: DERMASON 0.000000000   1.0000000            NaN      0.7394608        NA
## Class: HOROZ    0.979238754   0.4031982      0.2131024      0.9915730 0.2131024
## Class: SEKER    0.001644737   1.0000000      1.0000000      0.8511890 1.0000000
## Class: SIRA     0.406329114   0.9963526      0.9639640      0.8748332 0.9639640
##                      Recall          F1 Prevalence Detection Rate
## Class: BARBUNYA 0.901515152 0.775244300 0.09705882    0.087500000
## Class: BOMBAY   0.391025641 0.562211982 0.03823529    0.014950980
## Class: CALI     0.950920245 0.936555891 0.11985294    0.113970588
## Class: DERMASON 0.000000000          NA 0.26053922    0.000000000
## Class: HOROZ    0.979238754 0.350030921 0.14166667    0.138725490
## Class: SEKER    0.001644737 0.003284072 0.14901961    0.000245098
## Class: SIRA     0.406329114 0.571682992 0.19362745    0.078676471
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA          0.128676471         0.9279563
## Class: BOMBAY            0.014950980         0.6955128
## Class: CALI              0.123529412         0.9700299
## Class: DERMASON          0.000000000         0.5000000
## Class: HOROZ             0.650980392         0.6912185
## Class: SEKER             0.000245098         0.5008224
## Class: SIRA              0.081617647         0.7013408
db_tda_pc_5.40.5_n3_db_svm_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n3_db_svm_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_pca_5.40.5_svm_n3_3_fold<-(db_svm_fit_re - db_tda_pc_5.40.5_n3_svm_fit_re)
diff_drybean_tda_pca_5.40.5_svm_n3_3_fold
##      Accuracy
## 1 -0.01932403
## 2  0.38156684
## 3  0.01937659
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_svm.n3_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_svm_n3_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_svm.n3_3_fold
## $probLeft
## [1] 0.25
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_svm.n3_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_svm.n3_3_fold$probLeft/bst_dbf_db_tda_pca_5.40.5_svm.n3_3_fold$probRight
bst_dbf_db_tda_pca_5.40.5_svm.n3_3_fold_odds.left
## [1] 0.5
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_svm.n3_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_svm_n3_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_svm.n3_3_fold
## $winLeft
## [1] 0.08196667
## 
## $winRope
## [1] 0.2419333
## 
## $winRight
## [1] 0.6761
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_svm.n3_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_svm_n3_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_svm.n3_3_fold
## $left
## [1] 0.2251276
## 
## $rope
## [1] 0.02984633
## 
## $right
## [1] 0.745026
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.40.5_svm_n3_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_rf.n3_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_svm_n3_3_fold))
#bf_tda_pca_5.40.5_rf.n3_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.40.5_svm_n3_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_pca_5.40.5_svm_n3_3_fold)
## t = 0.99637, df = 2, p-value = 0.424
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.4221129  0.6765258
## sample estimates:
## mean of x 
## 0.1272065
### Test set diff
diff_drybean_tda_pca_5.40.5_svm.n3_test<-(db_svm_cf_ov_acc - db_tda_pc_5.40.5_n3_db_svm_cf0_ov_acc)
diff_drybean_tda_pca_5.40.5_svm.n3_test
##  Accuracy 
## 0.4933824
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_svm.n3_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_svm.n3_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_svm.n3_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_svm.n3_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_svm.n3_test$probLeft/bst_dbf_db_tda_pca_5.40.5_svm.n3_test$probRight
bst_dbf_db_tda_pca_5.40.5_svm.n3_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_svm.n3_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_svm.n3_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_svm.n3_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1625667
## 
## $winRight
## [1] 0.8374333
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_svm.n3_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_svm.n3_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_svm.n3_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_svm.n3_test)))

#BayesFactor
#bf_tda_pca_5.40.5_svm.n3_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_svm.n3_test)) #bf_tda_pca_5.40.5_svm.n3_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_svm.n3_test))


##Node4

DryBean_TDA_PC_5.40.5_n4_SvmFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.40.5.n4.vec, 
                    Importance = T,
                    method = 'svmRadial', 
                  trControl = fitControl,
                          tuneGrid = svmGrid, preProc = c('center','scale'),
                          metric='Accuracy')

DryBean_TDA_PC_5.40.5_n4_SvmFit0
## Support Vector Machines with Radial Basis Function Kernel 
## 
## 894 samples
##  16 predictor
##   4 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'HOROZ' 
## 
## Pre-processing: centered (16), scaled (16) 
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 595, 597, 596 
## Resampling results across tuning parameters:
## 
##   sigma  C     Accuracy   Kappa     
##    0.1   0.25  0.9832251  0.97273865
##    0.1   0.50  0.9821065  0.97093742
##    0.1   0.75  0.9843512  0.97464466
##    0.1   1.00  0.9832251  0.97279842
##    0.1   1.25  0.9843399  0.97460243
##    1.0   0.25  0.8423032  0.71760681
##    1.0   0.50  0.8959723  0.82027897
##    1.0   0.75  0.9328704  0.88721852
##    1.0   1.00  0.9463046  0.91075313
##    1.0   1.25  0.9463046  0.91075313
##   10.0   0.25  0.5055929  0.00000000
##   10.0   0.50  0.5055929  0.00000000
##   10.0   0.75  0.5167599  0.02632514
##   10.0   1.00  0.5603731  0.12984849
##   10.0   1.25  0.5861041  0.18792896
## 
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.1 and C = 0.75.
DryBean_TDA_PC_5.40.5_n4_SvmFit0$resample
##    Accuracy     Kappa Resample
## 1 0.9799331 0.9675922    Fold1
## 2 0.9898990 0.9835818    Fold2
## 3 0.9832215 0.9727600    Fold3
db_tda_pc_5.40.5_n4_svm_fit_re<-DryBean_TDA_PC_5.40.5_n4_SvmFit0 $resample[1]

summary(DryBean_TDA_PC_5.40.5_n4_SvmFit0)
## Length  Class   Mode 
##      1   ksvm     S4
#vip(DryBean_TDA_PC_5.40.5_n4_SvmFit0,25) + ggtitle("Adult_TDA_PCA_5.40.5_n4_SvmFit TDA-Assited Svm")

# Predict outcome using DryBean_TDA_PC_5.40.5_n4_SvmFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.40.5_n4_SvmFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.40.5_n4_db_svm_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length

## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
db_tda_pc_5.40.5_n4_db_svm_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      220      0    5        0     1     0    0
##   BOMBAY         23    156    5        0     0     0    0
##   CALI           43      0  459        0    37     0    5
##   DERMASON        0      0    0        0     0     0    0
##   HOROZ         110      0   20     1063   540   608  785
##   SEKER           0      0    0        0     0     0    0
##   SIRA            0      0    0        0     0     0    0
## 
## Overall Statistics
##                                           
##                Accuracy : 0.337           
##                  95% CI : (0.3225, 0.3517)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.2365          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.55556       1.00000      0.9387          0.0000
## Specificity                  0.99837       0.99286      0.9763          1.0000
## Pos Pred Value               0.97345       0.84783      0.8437             NaN
## Neg Pred Value               0.95433       1.00000      0.9915          0.7395
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.05392       0.03824      0.1125          0.0000
## Detection Prevalence         0.05539       0.04510      0.1333          0.0000
## Balanced Accuracy            0.77696       0.99643      0.9575          0.5000
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9343        0.000      0.0000
## Specificity                0.2616        1.000      1.0000
## Pos Pred Value             0.1727          NaN         NaN
## Neg Pred Value             0.9602        0.851      0.8064
## Prevalence                 0.1417        0.149      0.1936
## Detection Rate             0.1324        0.000      0.0000
## Detection Prevalence       0.7662        0.000      0.0000
## Balanced Accuracy          0.5979        0.500      0.5000
db_tda_pc_5.40.5_n4_db_svm_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      220      0    5        0     1     0    0
##   BOMBAY         23    156    5        0     0     0    0
##   CALI           43      0  459        0    37     0    5
##   DERMASON        0      0    0        0     0     0    0
##   HOROZ         110      0   20     1063   540   608  785
##   SEKER           0      0    0        0     0     0    0
##   SIRA            0      0    0        0     0     0    0
## 
## Overall Statistics
##                                           
##                Accuracy : 0.337           
##                  95% CI : (0.3225, 0.3517)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.2365          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.55556       1.00000      0.9387          0.0000
## Specificity                  0.99837       0.99286      0.9763          1.0000
## Pos Pred Value               0.97345       0.84783      0.8437             NaN
## Neg Pred Value               0.95433       1.00000      0.9915          0.7395
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.05392       0.03824      0.1125          0.0000
## Detection Prevalence         0.05539       0.04510      0.1333          0.0000
## Balanced Accuracy            0.77696       0.99643      0.9575          0.5000
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9343        0.000      0.0000
## Specificity                0.2616        1.000      1.0000
## Pos Pred Value             0.1727          NaN         NaN
## Neg Pred Value             0.9602        0.851      0.8064
## Prevalence                 0.1417        0.149      0.1936
## Detection Rate             0.1324        0.000      0.0000
## Detection Prevalence       0.7662        0.000      0.0000
## Balanced Accuracy          0.5979        0.500      0.5000
db_tda_pc_5.40.5_n4_db_svm_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   3.370098e-01   2.365183e-01   3.225041e-01   3.517493e-01   2.605392e-01 
## AccuracyPValue  McnemarPValue 
##   1.537231e-27            NaN
db_tda_pc_5.40.5_n4_db_svm_cf0_ov_acc<-db_tda_pc_5.40.5_n4_db_svm_cf0$overall[1]
db_tda_pc_5.40.5_n4_db_svm_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.5555556   0.9983713      0.9734513      0.9543332 0.9734513
## Class: BOMBAY     1.0000000   0.9928644      0.8478261      1.0000000 0.8478261
## Class: CALI       0.9386503   0.9763297      0.8437500      0.9915158 0.8437500
## Class: DERMASON   0.0000000   1.0000000            NaN      0.7394608        NA
## Class: HOROZ      0.9342561   0.2615648      0.1727447      0.9601677 0.1727447
## Class: SEKER      0.0000000   1.0000000            NaN      0.8509804        NA
## Class: SIRA       0.0000000   1.0000000            NaN      0.8063725        NA
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.5555556 0.7073955 0.09705882     0.05392157
## Class: BOMBAY   1.0000000 0.9176471 0.03823529     0.03823529
## Class: CALI     0.9386503 0.8886738 0.11985294     0.11250000
## Class: DERMASON 0.0000000        NA 0.26053922     0.00000000
## Class: HOROZ    0.9342561 0.2915767 0.14166667     0.13235294
## Class: SEKER    0.0000000        NA 0.14901961     0.00000000
## Class: SIRA     0.0000000        NA 0.19362745     0.00000000
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA           0.05539216         0.7769634
## Class: BOMBAY             0.04509804         0.9964322
## Class: CALI               0.13333333         0.9574900
## Class: DERMASON           0.00000000         0.5000000
## Class: HOROZ              0.76617647         0.5979104
## Class: SEKER              0.00000000         0.5000000
## Class: SIRA               0.00000000         0.5000000
db_tda_pc_5.40.5_n4_db_svm_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n4_db_svm_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_pca_5.40.5_svm_n4_3_fold<-(db_svm_fit_re - db_tda_pc_5.40.5_n4_svm_fit_re)
diff_drybean_tda_pca_5.40.5_svm_n4_3_fold
##      Accuracy
## 1 -0.05138416
## 2 -0.06042796
## 3 -0.04772746
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_svm.n4_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_svm_n4_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_svm.n4_3_fold
## $probLeft
## [1] 0.75
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_svm.n4_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_svm.n4_3_fold$probLeft/bst_dbf_db_tda_pca_5.40.5_svm.n4_3_fold$probRight
bst_dbf_db_tda_pca_5.40.5_svm.n4_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_svm.n4_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_svm_n4_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_svm.n4_3_fold
## $winLeft
## [1] 0.9914667
## 
## $winRope
## [1] 0.008533333
## 
## $winRight
## [1] 0
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_svm.n4_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_svm_n4_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_svm.n4_3_fold
## $left
## [1] 0.9949821
## 
## $rope
## [1] 0.002655173
## 
## $right
## [1] 0.002362758
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.40.5_svm_n4_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_rf.n4_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_svm_n4_3_fold))
#bf_tda_pca_5.40.5_rf.n4_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.40.5_svm_n4_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_pca_5.40.5_svm_n4_3_fold)
## t = -14.089, df = 2, p-value = 0.005
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.06942089 -0.03693883
## sample estimates:
##   mean of x 
## -0.05317986
### Test set diff
diff_drybean_tda_pca_5.40.5_svm.n4_test<-(db_svm_cf_ov_acc - db_tda_pc_5.40.5_n4_db_svm_cf0_ov_acc)
diff_drybean_tda_pca_5.40.5_svm.n4_test
##  Accuracy 
## 0.5904412
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_svm.n4_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_svm.n4_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_svm.n4_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_svm.n4_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_svm.n4_test$probLeft/bst_dbf_db_tda_pca_5.40.5_svm.n4_test$probRight
bst_dbf_db_tda_pca_5.40.5_svm.n4_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_svm.n4_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_svm.n4_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_svm.n4_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1605
## 
## $winRight
## [1] 0.8395
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_svm.n4_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_svm.n4_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_svm.n4_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_svm.n4_test)))

#BayesFactor
#bf_tda_pca_5.40.5_svm.n4_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_svm.n4_test)) #bf_tda_pca_5.40.5_svm.n4_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_svm.n4_test))

##Node5

#DryBean_TDA_PC_5.40.5_n5_SvmFit0 <- train(as.factor(Class) ~ ., data = #tda.m_dry_bean_dataset_5.40.5.n5.vec, 
#                   Importance = T,
#                   method = 'svmRadial', 
#                  trControl = fitControl,
#                         tuneGrid = svmGrid, preProc = c('center','scale'),
#                         metric='Accuracy')

#DryBean_TDA_PC_5.40.5_n5_SvmFit0
#DryBean_TDA_PC_5.40.5_n5_SvmFit0$resample
#db_tda_pc_5.40.5_n5_svm_fit_re<-DryBean_TDA_PC_5.40.5_n5_SvmFit0 $resample[1]

#summary(DryBean_TDA_PC_5.40.5_n5_SvmFit0)
#vip(DryBean_TDA_PC_5.40.5_n5_SvmFit0,25) + ggtitle("Adult_TDA_PCA_5.40.5_n5_SvmFit TDA-Assited Svm")

# Predict outcome using DryBean_TDA_PC_5.40.5_n5_SvmFit0 from training data based on testing data
#pred0 <- predict(DryBean_TDA_PC_5.40.5_n5_SvmFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
#db_tda_pc_5.40.5_n5_db_svm_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
#db_tda_pc_5.40.5_n5_db_svm_cf0
#db_tda_pc_5.40.5_n5_db_svm_cf0 
#db_tda_pc_5.40.5_n5_db_svm_cf0$overall
#db_tda_pc_5.40.5_n5_db_svm_cf0_ov_acc<-db_tda_pc_5.40.5_n5_db_svm_cf0$overall[1]
#db_tda_pc_5.40.5_n5_db_svm_cf0$byClass
#db_tda_pc_5.40.5_n5_db_svm_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n5_db_svm_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

#diff_drybean_tda_pca_5.40.5_svm_n5_3_fold<-(db_svm_fit_re - db_tda_pc_5.40.5_n5_svm_fit_re)
#diff_drybean_tda_pca_5.40.5_svm_n5_3_fold

## Bayesian Tests 3-fold diff

# Bayesian Sign Test

#bst_dbf_db_tda_pca_5.40.5_svm.n5_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_svm_n5_3_fold),-0.01,0.01)
#bst_dbf_db_tda_pca_5.40.5_svm.n5_3_fold

# Odds Left Bayesian Sign Test 

#bst_dbf_db_tda_pca_5.40.5_svm.n5_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_svm.n5_3_fold$probLeft/bst_dbf_db_tda_pca_5.40.5_svm.n5_3_fold$probRight
#bst_dbf_db_tda_pca_5.40.5_svm.n5_3_fold_odds.left

# Bayesian Signed Rank Test

##bsr_dbf_db_tda_pca_5.40.5_svm.n5_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_svm_n5_3_fold),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.40.5_svm.n5_3_fold

# Bayesian Correlated Test

#bct_dbf_db_tda_pca_5.40.5_svm.n5_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_svm_n5_3_fold),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.40.5_svm.n5_3_fold

# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_svm_n5_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_rf.n5_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_svm_n5_3_fold))
#bf_tda_pca_5.40.5_rf.n5_3_fold

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_svm_n5_3_fold))


### Test set diff
#diff_drybean_tda_pca_5.40.5_svm.n5_test<-(db_svm_cf_ov_acc - db_tda_pc_5.40.5_n5_db_svm_cf0_ov_acc)
#diff_drybean_tda_pca_5.40.5_svm.n5_test


## Bayesian Tests Test set diff

# Bayesian Sign Test

#bst_dbf_db_tda_pca_5.40.5_svm.n5_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_svm.n5_test),-0.01,0.01)
#bst_dbf_db_tda_pca_5.40.5_svm.n5_test

# Odds Left Bayesian Sign Test 

#bst_dbf_db_tda_pca_5.40.5_svm.n5_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_svm.n5_test$probLeft/bst_dbf_db_tda_pca_5.40.5_svm.n5_test$probRight
#bst_dbf_db_tda_pca_5.40.5_svm.n5_test_odds.left

# Bayesian Signed Rank Test

#bsr_dbf_db_tda_pca_5.40.5_svm.n5_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_svm.n5_test),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.40.5_svm.n5_test

# Bayesian Correlated Test

#bct_dbf_db_tda_pca_5.40.5_svm.n5_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_svm.n5_test),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.40.5_svm.n5_test

# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_svm.n5_test)))

#BayesFactor
#bf_tda_pca_5.40.5_svm.n5_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_svm.n5_test)) #bf_tda_pca_5.40.5_svm.n5_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_svm.n5_test))


#With TDA KDE filter 5 intervals, 50% overlap, 5 bins 
##Node1


DryBean_TDA_KDE_5.40.5_n1_SvmFit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.40.5.n1.vec, 
                    Importance = T,
                    method = 'svmRadial', 
                  trControl = fitControl,
                          tuneGrid = svmGrid, preProc = c('center','scale'),
                          metric='Accuracy')

DryBean_TDA_KDE_5.40.5_n1_SvmFit0
## Support Vector Machines with Radial Basis Function Kernel 
## 
## 7503 samples
##   16 predictor
##    7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## Pre-processing: centered (16), scaled (16) 
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 5002, 5001, 5003 
## Resampling results across tuning parameters:
## 
##   sigma  C     Accuracy   Kappa     
##    0.1   0.25  0.9502873  0.94009439
##    0.1   0.50  0.9513534  0.94138944
##    0.1   0.75  0.9514867  0.94155689
##    0.1   1.00  0.9528191  0.94316330
##    0.1   1.25  0.9528191  0.94316484
##    1.0   0.25  0.9220306  0.90561266
##    1.0   0.50  0.9342930  0.92059761
##    1.0   0.75  0.9404232  0.92807209
##    1.0   1.00  0.9436215  0.93196080
##    1.0   1.25  0.9434885  0.93180782
##   10.0   0.25  0.2822880  0.07457811
##   10.0   0.50  0.4479573  0.29637229
##   10.0   0.75  0.5616449  0.44593413
##   10.0   1.00  0.6457433  0.55541192
##   10.0   1.25  0.6656020  0.58140156
## 
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.1 and C = 1.
DryBean_TDA_KDE_5.40.5_n1_SvmFit0$resample
##    Accuracy     Kappa Resample
## 1 0.9532187 0.9436318    Fold1
## 2 0.9516387 0.9417590    Fold2
## 3 0.9536000 0.9440991    Fold3
ad_tda_kde_5.40.5_n1_svm_fit_re<-DryBean_TDA_KDE_5.40.5_n1_SvmFit0 $resample[1]

summary(DryBean_TDA_PC_5.40.5_n1_SvmFit0)
## Length  Class   Mode 
##      1   ksvm     S4
#vip(DryBean_TDA_KDE_5.40.5_n1_SvmFit0,25) + ggtitle("DryBean_TDA_KDE_5.40.5_n1_SvmFit TDA-Assited Svm")

# Predict outcome using DryBean_TDA_KDE_5.40.5_n1_SvmFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.40.5_n1_SvmFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
ad_tda_kde_5.40.5_n1_db_svm_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
ad_tda_kde_5.40.5_n1_db_svm_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      350      0   11        1     2     2    4
##   BOMBAY          0    156    0        0     0     0    0
##   CALI           25      0  462        0     9     0    0
##   DERMASON        0      0    0      835     2     3   21
##   HOROZ           3      0    8        2   559     0   10
##   SEKER           5      0    1       27     0   585   11
##   SIRA           13      0    7      198     6    18  744
## 
## Overall Statistics
##                                           
##                Accuracy : 0.9047          
##                  95% CI : (0.8952, 0.9135)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.8852          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.88384       1.00000      0.9448          0.7855
## Specificity                  0.99457       1.00000      0.9905          0.9914
## Pos Pred Value               0.94595       1.00000      0.9315          0.9698
## Neg Pred Value               0.98760       1.00000      0.9925          0.9292
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.08578       0.03824      0.1132          0.2047
## Detection Prevalence         0.09069       0.03824      0.1216          0.2110
## Balanced Accuracy            0.93920       1.00000      0.9677          0.8884
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9671       0.9622      0.9418
## Specificity                0.9934       0.9873      0.9264
## Pos Pred Value             0.9605       0.9300      0.7546
## Neg Pred Value             0.9946       0.9933      0.9851
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1370       0.1434      0.1824
## Detection Prevalence       0.1426       0.1542      0.2417
## Balanced Accuracy          0.9803       0.9747      0.9341
ad_tda_kde_5.40.5_n1_db_svm_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      350      0   11        1     2     2    4
##   BOMBAY          0    156    0        0     0     0    0
##   CALI           25      0  462        0     9     0    0
##   DERMASON        0      0    0      835     2     3   21
##   HOROZ           3      0    8        2   559     0   10
##   SEKER           5      0    1       27     0   585   11
##   SIRA           13      0    7      198     6    18  744
## 
## Overall Statistics
##                                           
##                Accuracy : 0.9047          
##                  95% CI : (0.8952, 0.9135)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.8852          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.88384       1.00000      0.9448          0.7855
## Specificity                  0.99457       1.00000      0.9905          0.9914
## Pos Pred Value               0.94595       1.00000      0.9315          0.9698
## Neg Pred Value               0.98760       1.00000      0.9925          0.9292
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.08578       0.03824      0.1132          0.2047
## Detection Prevalence         0.09069       0.03824      0.1216          0.2110
## Balanced Accuracy            0.93920       1.00000      0.9677          0.8884
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9671       0.9622      0.9418
## Specificity                0.9934       0.9873      0.9264
## Pos Pred Value             0.9605       0.9300      0.7546
## Neg Pred Value             0.9946       0.9933      0.9851
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1370       0.1434      0.1824
## Detection Prevalence       0.1426       0.1542      0.2417
## Balanced Accuracy          0.9803       0.9747      0.9341
ad_tda_kde_5.40.5_n1_db_svm_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.9046569      0.8851577      0.8952306      0.9134989      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
ad_tda_kde_5.40.5_n1_db_svm_cf0_ov_acc<-ad_tda_kde_5.40.5_n1_db_svm_cf0$overall[1]
ad_tda_kde_5.40.5_n1_db_svm_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.8838384   0.9945711      0.9459459      0.9876011 0.9459459
## Class: BOMBAY     1.0000000   1.0000000      1.0000000      1.0000000 1.0000000
## Class: CALI       0.9447853   0.9905319      0.9314516      0.9924665 0.9314516
## Class: DERMASON   0.7855127   0.9913822      0.9698026      0.9291705 0.9698026
## Class: HOROZ      0.9671280   0.9934323      0.9604811      0.9945683 0.9604811
## Class: SEKER      0.9621711   0.9873272      0.9300477      0.9933353 0.9300477
## Class: SIRA       0.9417722   0.9264438      0.7545639      0.9851325 0.7545639
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.8838384 0.9138381 0.09705882     0.08578431
## Class: BOMBAY   1.0000000 1.0000000 0.03823529     0.03823529
## Class: CALI     0.9447853 0.9380711 0.11985294     0.11323529
## Class: DERMASON 0.7855127 0.8679834 0.26053922     0.20465686
## Class: HOROZ    0.9671280 0.9637931 0.14166667     0.13700980
## Class: SEKER    0.9621711 0.9458367 0.14901961     0.14338235
## Class: SIRA     0.9417722 0.8378378 0.19362745     0.18235294
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA           0.09068627         0.9392048
## Class: BOMBAY             0.03823529         1.0000000
## Class: CALI               0.12156863         0.9676586
## Class: DERMASON           0.21102941         0.8884474
## Class: HOROZ              0.14264706         0.9802802
## Class: SEKER              0.15416667         0.9747491
## Class: SIRA               0.24166667         0.9341080
ad_tda_kde_5.40.5_n1_db_svm_cf0_pre_rec_f1<-ad_tda_kde_5.40.5_n1_db_svm_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_kde_5.40.5_svm_n1_3_fold<-(db_svm_fit_re - ad_tda_kde_5.40.5_n1_svm_fit_re)
diff_drybean_tda_kde_5.40.5_svm_n1_3_fold
##      Accuracy
## 1 -0.02466977
## 2 -0.02216766
## 3 -0.01810598
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_svm.n1_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_svm_n1_3_fold),-0.01,0.01)
bst_tda_kde_5.40.5_svm.n1_3_fold
## $probLeft
## [1] 0.75
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_svm.n1_3_fold_odds.left<-bst_tda_kde_5.40.5_svm.n1_3_fold$probLeft/bst_tda_kde_5.40.5_svm.n1_3_fold$probRight
bst_tda_kde_5.40.5_svm.n1_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_svm.n1_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_svm_n1_3_fold),-0.01,0.01)
bsr_tda_kde_5.40.5_svm.n1_3_fold
## $winLeft
## [1] 0.9640333
## 
## $winRope
## [1] 0.03596667
## 
## $winRight
## [1] 0
# Bayesian Correlated Test

bct_tda_kde_5.40.5_svm.n1_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_svm_n1_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_svm.n1_3_fold
## $left
## [1] 0.9829405
## 
## $rope
## [1] 0.01464241
## 
## $right
## [1] 0.002417064
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.40.5_svm_n1_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_kde_5.40.5_svm.n1_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_svm_n1_3_fold))
#bf_tda_kde_5.40.5_svm.n1_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.40.5_svm_n1_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_kde_5.40.5_svm_n1_3_fold)
## t = -11.319, df = 2, p-value = 0.007715
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.02987683 -0.01341877
## sample estimates:
##  mean of x 
## -0.0216478
### Test set diff
diff_drybean_tda_kde_5.40.5_svm.n1_test<-(db_svm_cf_ov_acc-ad_tda_kde_5.40.5_n1_db_svm_cf0_ov_acc)
diff_drybean_tda_kde_5.40.5_svm.n1_test
##   Accuracy 
## 0.02279412
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_svm.n1_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_svm.n1_test),-0.01,0.01)
bst_tda_kde_5.40.5_svm.n1_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_svm.n1_test_odds.left<-bst_tda_kde_5.40.5_svm.n1_test$probLeft/bst_tda_kde_5.40.5_svm.n1_test$probRight
bst_tda_kde_5.40.5_svm.n1_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_svm.n1_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_svm.n1_test),-0.01,0.01)
bsr_tda_kde_5.40.5_svm.n1_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1624
## 
## $winRight
## [1] 0.8376
# Bayesian Correlated Test

bct_tda_kde_5.40.5_svm.n1_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_svm.n1_test),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_svm.n1_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_svm.n1_test))

#BayesFactor
#bf_tda_kde_5.40.5_svm.n1_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_svm.n1_test)) #bf_tda_kde_5.40.5_svm.n1_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_svm.n1_test))


##With TDA PCA filter 5 intervals, 50% overlap, 5 bins 
##Node2

DryBean_TDA_KDE_5.40.5_n2_SvmFit0 <- train(as.factor(Class) ~ ., data =  tda.m_kde_dry_bean_dataset_5.40.5.n2.vec, 
                    Importance = T,
                    method = 'svmRadial', 
                  trControl = fitControl,
                          tuneGrid = svmGrid, preProc = c('center','scale'),
                          metric='Accuracy')

DryBean_TDA_KDE_5.40.5_n2_SvmFit0
## Support Vector Machines with Radial Basis Function Kernel 
## 
## 7002 samples
##   16 predictor
##    6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## Pre-processing: centered (16), scaled (16) 
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 4668, 4668, 4668 
## Resampling results across tuning parameters:
## 
##   sigma  C     Accuracy   Kappa    
##    0.1   0.25  0.9450157  0.9291274
##    0.1   0.50  0.9465867  0.9311982
##    0.1   0.75  0.9467295  0.9314036
##    0.1   1.00  0.9484433  0.9336262
##    0.1   1.25  0.9487289  0.9339972
##    1.0   0.25  0.9324479  0.9130216
##    1.0   0.50  0.9367324  0.9186424
##    1.0   0.75  0.9411597  0.9243486
##    1.0   1.00  0.9420166  0.9254547
##    1.0   1.25  0.9424450  0.9260017
##   10.0   0.25  0.5488432  0.3681545
##   10.0   0.50  0.5971151  0.4404817
##   10.0   0.75  0.6375321  0.4995681
##   10.0   1.00  0.6730934  0.5512097
##   10.0   1.25  0.6918023  0.5786817
## 
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.1 and C = 1.25.
DryBean_TDA_KDE_5.40.5_n2_SvmFit0$resample
##    Accuracy     Kappa Resample
## 1 0.9532991 0.9399776    Fold1
## 2 0.9481577 0.9332709    Fold3
## 3 0.9447301 0.9287431    Fold2
ad_tda_kde_5.40.5_n2_svm_fit_re<-DryBean_TDA_KDE_5.40.5_n2_SvmFit0$resample[1]

summary(DryBean_TDA_KDE_5.40.5_n2_SvmFit0)
## Length  Class   Mode 
##      1   ksvm     S4
#vip(DryBean_TDA_KDE_5.40.5_n2_SvmFit0,25) + ggtitle("DryBean_TDA_KDE_5.40.5_n2_SvmFit TDA-Assited Svm")
# Predict outcome using DryBean_TDA_KDE_5.40.5_n2_SvmFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.40.5_n2_SvmFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
ad_tda_kde_5.40.5_n2_db_svm_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length

## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
ad_tda_kde_5.40.5_n2_db_svm_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      312      0   11        0     2     3    3
##   BOMBAY          0      0    0        0     0     0    0
##   CALI           36      0  453        0    18     0    0
##   DERMASON        0      0    0      943     4     8   44
##   HOROZ          26    156   14        1   547     0   10
##   SEKER           7      0    1       12     0   578   11
##   SIRA           15      0   10      107     7    19  722
## 
## Overall Statistics
##                                           
##                Accuracy : 0.8713          
##                  95% CI : (0.8607, 0.8815)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.8437          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.78788       0.00000      0.9264          0.8871
## Specificity                  0.99484       1.00000      0.9850          0.9814
## Pos Pred Value               0.94260           NaN      0.8935          0.9439
## Neg Pred Value               0.97759       0.96176      0.9899          0.9611
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.07647       0.00000      0.1110          0.2311
## Detection Prevalence         0.08113       0.00000      0.1243          0.2449
## Balanced Accuracy            0.89136       0.50000      0.9557          0.9343
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9464       0.9507      0.9139
## Specificity                0.9409       0.9911      0.9520
## Pos Pred Value             0.7255       0.9491      0.8205
## Neg Pred Value             0.9907       0.9914      0.9787
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1341       0.1417      0.1770
## Detection Prevalence       0.1848       0.1493      0.2157
## Balanced Accuracy          0.9436       0.9709      0.9329
ad_tda_kde_5.40.5_n2_db_svm_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      312      0   11        0     2     3    3
##   BOMBAY          0      0    0        0     0     0    0
##   CALI           36      0  453        0    18     0    0
##   DERMASON        0      0    0      943     4     8   44
##   HOROZ          26    156   14        1   547     0   10
##   SEKER           7      0    1       12     0   578   11
##   SIRA           15      0   10      107     7    19  722
## 
## Overall Statistics
##                                           
##                Accuracy : 0.8713          
##                  95% CI : (0.8607, 0.8815)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.8437          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.78788       0.00000      0.9264          0.8871
## Specificity                  0.99484       1.00000      0.9850          0.9814
## Pos Pred Value               0.94260           NaN      0.8935          0.9439
## Neg Pred Value               0.97759       0.96176      0.9899          0.9611
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.07647       0.00000      0.1110          0.2311
## Detection Prevalence         0.08113       0.00000      0.1243          0.2449
## Balanced Accuracy            0.89136       0.50000      0.9557          0.9343
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9464       0.9507      0.9139
## Specificity                0.9409       0.9911      0.9520
## Pos Pred Value             0.7255       0.9491      0.8205
## Neg Pred Value             0.9907       0.9914      0.9787
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1341       0.1417      0.1770
## Detection Prevalence       0.1848       0.1493      0.2157
## Balanced Accuracy          0.9436       0.9709      0.9329
ad_tda_kde_5.40.5_n2_db_svm_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.8713235      0.8436973      0.8606602      0.8814519      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
ad_tda_kde_5.40.5_n2_db_svm_cf0_ov_acc<-ad_tda_kde_5.40.5_n2_db_svm_cf0$overall[1]
ad_tda_kde_5.40.5_n2_db_svm_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.7878788   0.9948426      0.9425982      0.9775940 0.9425982
## Class: BOMBAY     0.0000000   1.0000000            NaN      0.9617647        NA
## Class: CALI       0.9263804   0.9849624      0.8934911      0.9899244 0.8934911
## Class: DERMASON   0.8871119   0.9814385      0.9439439      0.9610516 0.9439439
## Class: HOROZ      0.9463668   0.9408909      0.7254642      0.9906795 0.7254642
## Class: SEKER      0.9506579   0.9910714      0.9490969      0.9913570 0.9490969
## Class: SIRA       0.9139241   0.9519757      0.8204545      0.9787500 0.8204545
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.7878788 0.8583219 0.09705882     0.07647059
## Class: BOMBAY   0.0000000        NA 0.03823529     0.00000000
## Class: CALI     0.9263804 0.9096386 0.11985294     0.11102941
## Class: DERMASON 0.8871119 0.9146460 0.26053922     0.23112745
## Class: HOROZ    0.9463668 0.8213213 0.14166667     0.13406863
## Class: SEKER    0.9506579 0.9498767 0.14901961     0.14166667
## Class: SIRA     0.9139241 0.8646707 0.19362745     0.17696078
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA           0.08112745         0.8913607
## Class: BOMBAY             0.00000000         0.5000000
## Class: CALI               0.12426471         0.9556714
## Class: DERMASON           0.24485294         0.9342752
## Class: HOROZ              0.18480392         0.9436289
## Class: SEKER              0.14926471         0.9708647
## Class: SIRA               0.21568627         0.9329499
ad_tda_kde_5.40.5_n2_db_svm_cf0_pre_rec_f1<-ad_tda_kde_5.40.5_n2_db_svm_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_kde_5.40.5_svm_n2_3_fold<-(db_svm_fit_re - ad_tda_kde_5.40.5_n2_svm_fit_re)
diff_drybean_tda_kde_5.40.5_svm_n2_3_fold
##       Accuracy
## 1 -0.024750112
## 2 -0.018686636
## 3 -0.009236056
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_svm.n2_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_svm_n2_3_fold),-0.01,0.01)
bst_tda_kde_5.40.5_svm.n2_3_fold
## $probLeft
## [1] 0.5
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_svm.n2_3_fold_odds.left<-bst_tda_kde_5.40.5_svm.n2_3_fold$probLeft/bst_tda_kde_5.40.5_svm.n2_3_fold$probRight
bst_tda_kde_5.40.5_svm.n2_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_svm.n2_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_svm_n2_3_fold),-0.01,0.01)
bsr_tda_kde_5.40.5_svm.n2_3_fold
## $winLeft
## [1] 0.7839667
## 
## $winRope
## [1] 0.2160333
## 
## $winRight
## [1] 0
# Bayesian Correlated Test

bct_tda_kde_5.40.5_svm.n2_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_svm_n2_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_svm.n2_3_fold
## $left
## [1] 0.8579384
## 
## $rope
## [1] 0.1250804
## 
## $right
## [1] 0.01698118
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.40.5_svm_n2_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_kde_5.40.5_svm.n2_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_svm_n2_3_fold))
#bf_tda_kde_5.40.5_svm.n2_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.40.5_svm_n2_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_kde_5.40.5_svm_n2_3_fold)
## t = -3.8896, df = 2, p-value = 0.06019
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.036979607  0.001864404
## sample estimates:
##  mean of x 
## -0.0175576
### Test set diff
diff_drybean_tda_kde_5.40.5_svm.n2_test<-(db_svm_cf_ov_acc-ad_tda_kde_5.40.5_n2_db_svm_cf0_ov_acc)
diff_drybean_tda_kde_5.40.5_svm.n2_test
##   Accuracy 
## 0.05612745
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_svm.n2_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_svm.n2_test),-0.01,0.01)
bst_tda_kde_5.40.5_svm.n2_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_svm.n2_test_odds.left<-bst_tda_kde_5.40.5_svm.n2_test$probLeft/bst_tda_kde_5.40.5_svm.n2_test$probRight
bst_tda_kde_5.40.5_svm.n2_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_svm.n2_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_svm.n2_test),-0.01,0.01)
bsr_tda_kde_5.40.5_svm.n2_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1562
## 
## $winRight
## [1] 0.8438
# Bayesian Correlated Test

bct_tda_kde_5.40.5_svm.n2_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_svm.n2_test),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_svm.n2_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_svm.n2_test))

#BayesFactor
#bf_tda_kde_5.40.5_svm.n2_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_svm.n2_test)) #bf_tda_kde_5.40.5_svm.n2_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_svm.n2_test))

##Node3

DryBean_TDA_KDE_5.40.5_n3_SvmFit0 <- train(as.factor(Class) ~ ., data =  tda.m_kde_dry_bean_dataset_5.40.5.n3.vec, 
                    Importance = T,
                    method = 'svmRadial', 
                  trControl = fitControl,
                          tuneGrid = svmGrid, preProc = c('center','scale'),
                          metric='Accuracy')

DryBean_TDA_KDE_5.40.5_n3_SvmFit0
## Support Vector Machines with Radial Basis Function Kernel 
## 
## 3511 samples
##   16 predictor
##    6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## Pre-processing: centered (16), scaled (16) 
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 2341, 2340, 2341 
## Resampling results across tuning parameters:
## 
##   sigma  C     Accuracy   Kappa     
##    0.1   0.25  0.6068354  0.53500483
##    0.1   0.50  0.6099696  0.54063224
##    0.1   0.75  0.6116790  0.54350956
##    0.1   1.00  0.6116792  0.54372319
##    0.1   1.25  0.6116792  0.54375216
##    1.0   0.25  0.5925885  0.50921985
##    1.0   0.50  0.5982874  0.52007463
##    1.0   0.75  0.5991424  0.52211295
##    1.0   1.00  0.6005669  0.52470216
##    1.0   1.25  0.6008518  0.52515746
##   10.0   0.25  0.3429494  0.00000000
##   10.0   0.50  0.3515013  0.02093933
##   10.0   0.75  0.3791517  0.08792423
##   10.0   1.00  0.4350016  0.20004269
##   10.0   1.25  0.4486819  0.23093335
## 
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.1 and C = 1.
DryBean_TDA_KDE_5.40.5_n3_SvmFit0$resample
##      Accuracy      Kappa Resample
## 1 0.911111111  0.8650196    Fold1
## 2 0.005977797 -0.1093663    Fold2
## 3 0.917948718  0.8755163    Fold3
ad_tda_kde_5.40.5_n3_svm_fit_re<-DryBean_TDA_KDE_5.40.5_n3_SvmFit0 $resample[1]

summary(DryBean_TDA_KDE_5.40.5_n3_SvmFit0)
## Length  Class   Mode 
##      1   ksvm     S4
#vip(DryBean_TDA_KDE_5.40.5_n3_SvmFit0,25) + ggtitle("DryBean_TDA_KDE_5.40.5_n3_SvmFit TDA-Assited Svm")

# Predict outcome using DryBean_TDA_KDE_5.40.5_n3_SvmFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.40.5_n3_SvmFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
ad_tda_kde_5.40.5_n3_db_svm_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length

## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
ad_tda_kde_5.40.5_n3_db_svm_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        2      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     0    0
##   DERMASON      377    156  481     1003   272    49   85
##   HOROZ           1      0    6        0   294     0    4
##   SEKER           1      0    0       11     0   533    6
##   SIRA           15      0    2       49    12    26  695
## 
## Overall Statistics
##                                           
##                Accuracy : 0.6194          
##                  95% CI : (0.6043, 0.6343)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.5099          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                0.0050505       0.00000      0.0000          0.9436
## Specificity                1.0000000       1.00000      1.0000          0.5293
## Pos Pred Value             1.0000000           NaN         NaN          0.4139
## Neg Pred Value             0.9033840       0.96176      0.8801          0.9638
## Prevalence                 0.0970588       0.03824      0.1199          0.2605
## Detection Rate             0.0004902       0.00000      0.0000          0.2458
## Detection Prevalence       0.0004902       0.00000      0.0000          0.5939
## Balanced Accuracy          0.5025253       0.50000      0.5000          0.7364
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity               0.50865       0.8766      0.8797
## Specificity               0.99686       0.9948      0.9684
## Pos Pred Value            0.96393       0.9673      0.8698
## Neg Pred Value            0.92477       0.9787      0.9710
## Prevalence                0.14167       0.1490      0.1936
## Detection Rate            0.07206       0.1306      0.1703
## Detection Prevalence      0.07475       0.1350      0.1958
## Balanced Accuracy         0.75275       0.9357      0.9241
ad_tda_kde_5.40.5_n3_db_svm_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        2      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     0    0
##   DERMASON      377    156  481     1003   272    49   85
##   HOROZ           1      0    6        0   294     0    4
##   SEKER           1      0    0       11     0   533    6
##   SIRA           15      0    2       49    12    26  695
## 
## Overall Statistics
##                                           
##                Accuracy : 0.6194          
##                  95% CI : (0.6043, 0.6343)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.5099          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                0.0050505       0.00000      0.0000          0.9436
## Specificity                1.0000000       1.00000      1.0000          0.5293
## Pos Pred Value             1.0000000           NaN         NaN          0.4139
## Neg Pred Value             0.9033840       0.96176      0.8801          0.9638
## Prevalence                 0.0970588       0.03824      0.1199          0.2605
## Detection Rate             0.0004902       0.00000      0.0000          0.2458
## Detection Prevalence       0.0004902       0.00000      0.0000          0.5939
## Balanced Accuracy          0.5025253       0.50000      0.5000          0.7364
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity               0.50865       0.8766      0.8797
## Specificity               0.99686       0.9948      0.9684
## Pos Pred Value            0.96393       0.9673      0.8698
## Neg Pred Value            0.92477       0.9787      0.9710
## Prevalence                0.14167       0.1490      0.1936
## Detection Rate            0.07206       0.1306      0.1703
## Detection Prevalence      0.07475       0.1350      0.1958
## Balanced Accuracy         0.75275       0.9357      0.9241
ad_tda_kde_5.40.5_n3_db_svm_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.6193627      0.5098616      0.6042604      0.6342940      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
ad_tda_kde_5.40.5_n3_db_svm_cf0_ov_acc<-ad_tda_kde_5.40.5_n3_db_svm_cf0$overall[1]
ad_tda_kde_5.40.5_n3_db_svm_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.005050505   1.0000000      1.0000000      0.9033840 1.0000000
## Class: BOMBAY   0.000000000   1.0000000            NaN      0.9617647        NA
## Class: CALI     0.000000000   1.0000000            NaN      0.8801471        NA
## Class: DERMASON 0.943555974   0.5293338      0.4139496      0.9637900 0.4139496
## Class: HOROZ    0.508650519   0.9968589      0.9639344      0.9247682 0.9639344
## Class: SEKER    0.876644737   0.9948157      0.9673321      0.9787475 0.9673321
## Class: SIRA     0.879746835   0.9683891      0.8698373      0.9710454 0.8698373
##                      Recall         F1 Prevalence Detection Rate
## Class: BARBUNYA 0.005050505 0.01005025 0.09705882   0.0004901961
## Class: BOMBAY   0.000000000         NA 0.03823529   0.0000000000
## Class: CALI     0.000000000         NA 0.11985294   0.0000000000
## Class: DERMASON 0.943555974 0.57544464 0.26053922   0.2458333333
## Class: HOROZ    0.508650519 0.66591166 0.14166667   0.0720588235
## Class: SEKER    0.876644737 0.91975841 0.14901961   0.1306372549
## Class: SIRA     0.879746835 0.87476400 0.19362745   0.1703431373
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA         0.0004901961         0.5025253
## Class: BOMBAY           0.0000000000         0.5000000
## Class: CALI             0.0000000000         0.5000000
## Class: DERMASON         0.5938725490         0.7364449
## Class: HOROZ            0.0747549020         0.7527547
## Class: SEKER            0.1350490196         0.9357302
## Class: SIRA             0.1958333333         0.9240679
ad_tda_kde_5.40.5_n3_db_svm_cf0_pre_rec_f1<-ad_tda_kde_5.40.5_n3_db_svm_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_kde_5.40.5_svm_n3_3_fold<-(db_svm_fit_re - ad_tda_kde_5.40.5_n3_svm_fit_re)
diff_drybean_tda_kde_5.40.5_svm_n3_3_fold
##     Accuracy
## 1 0.01743783
## 2 0.92349324
## 3 0.01754530
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_svm.n3_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_svm_n3_3_fold),-0.01,0.01)
bst_tda_kde_5.40.5_svm.n3_3_fold
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_svm.n3_3_fold_odds.left<-bst_tda_kde_5.40.5_svm.n3_3_fold$probLeft/bst_tda_kde_5.40.5_svm.n3_3_fold$probRight
bst_tda_kde_5.40.5_svm.n3_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_svm.n3_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_svm_n3_3_fold),-0.01,0.01)
bsr_tda_kde_5.40.5_svm.n3_3_fold
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.09296667
## 
## $winRight
## [1] 0.9070333
# Bayesian Correlated Test

bct_tda_kde_5.40.5_svm.n3_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_svm_n3_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_svm.n3_3_fold
## $left
## [1] 0.2222324
## 
## $rope
## [1] 0.01198793
## 
## $right
## [1] 0.7657796
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.40.5_svm_n3_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_kde_5.40.5_svm.n3_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_svm_n3_3_fold))
#bf_tda_kde_5.40.5_svm.n3_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.40.5_svm_n3_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_kde_5.40.5_svm_n3_3_fold)
## t = 1.0579, df = 2, p-value = 0.401
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.9799114  1.6188956
## sample estimates:
## mean of x 
## 0.3194921
### Test set diff
diff_drybean_tda_kde_5.40.5_svm.n3_test<-(db_svm_cf_ov_acc-ad_tda_kde_5.40.5_n3_db_svm_cf0_ov_acc)
diff_drybean_tda_kde_5.40.5_svm.n3_test
##  Accuracy 
## 0.3080882
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_svm.n3_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_svm.n3_test),-0.01,0.01)
bst_tda_kde_5.40.5_svm.n3_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_svm.n3_test_odds.left<-bst_tda_kde_5.40.5_svm.n3_test$probLeft/bst_tda_kde_5.40.5_svm.n3_test$probRight
bst_tda_kde_5.40.5_svm.n3_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_svm.n3_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_svm.n3_test),-0.01,0.01)
bsr_tda_kde_5.40.5_svm.n3_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1594333
## 
## $winRight
## [1] 0.8405667
# Bayesian Correlated Test

bct_tda_kde_5.40.5_svm.n3_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_svm.n3_test),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_svm.n3_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_svm.n3_test))

#BayesFactor
#bf_tda_kde_5.40.5_svm.n3_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_svm.n3_test)) #bf_tda_kde_5.40.5_svm.n3_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_svm.n3_test))

##Node4

DryBean_TDA_KDE_5.40.5_n4_SvmFit0 <- train(as.factor(Class) ~ ., data =  tda.m_kde_dry_bean_dataset_5.40.5.n4.vec, 
                    Importance = T,
                    method = 'svmRadial', 
                  trControl = fitControl,
                          tuneGrid = svmGrid, preProc = c('center','scale'),
                          metric='Accuracy')

DryBean_TDA_KDE_5.40.5_n4_SvmFit0
## Support Vector Machines with Radial Basis Function Kernel 
## 
## 1759 samples
##   16 predictor
##    4 classes: 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## Pre-processing: centered (16), scaled (16) 
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 1173, 1173, 1172 
## Resampling results across tuning parameters:
## 
##   sigma  C     Accuracy   Kappa      
##    0.1   0.25  0.8158130  0.680258892
##    0.1   0.50  0.8180864  0.685876855
##    0.1   0.75  0.8158111  0.683351373
##    0.1   1.00  0.8152452  0.683724462
##    0.1   1.25  0.8124039  0.679251901
##    1.0   0.25  0.7345065  0.494820303
##    1.0   0.50  0.7652077  0.568169352
##    1.0   0.75  0.7822677  0.607898684
##    1.0   1.00  0.7913641  0.630768048
##    1.0   1.25  0.7964758  0.643254570
##   10.0   0.25  0.5321209  0.000000000
##   10.0   0.50  0.5321209  0.000000000
##   10.0   0.75  0.5326897  0.001538908
##   10.0   1.00  0.5378062  0.015322974
##   10.0   1.25  0.5491741  0.046353028
## 
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.1 and C = 0.5.
DryBean_TDA_KDE_5.40.5_n4_SvmFit0$resample
##    Accuracy     Kappa Resample
## 1 0.8040886 0.6641509    Fold3
## 2 0.8378840 0.7190484    Fold1
## 3 0.8122867 0.6744313    Fold2
ad_tda_kde_5.40.5_n4_svm_fit_re<-DryBean_TDA_KDE_5.40.5_n4_SvmFit0 $resample[1]

summary(DryBean_TDA_KDE_5.40.5_n4_SvmFit0)
## Length  Class   Mode 
##      1   ksvm     S4
#vip(DryBean_TDA_KDE_5.40.5_n4_SvmFit0,25) + ggtitle("DryBean_TDA_KDE_5.40.5_n_SvmFit TDA-Assited Svm")

# Predict outcome using DryBean_TDA_KDE_5.40.5_n4_SvmFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.40.5_n4_SvmFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
ad_tda_kde_5.40.5_n4_db_svm_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length

## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
ad_tda_kde_5.40.5_n4_db_svm_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     0    0
##   DERMASON      395    156  489     1024   578   228  580
##   HOROZ           0      0    0        0     0     0    0
##   SEKER           0      0    0        8     0   374    4
##   SIRA            1      0    0       31     0     6  206
## 
## Overall Statistics
##                                           
##                Accuracy : 0.3931          
##                  95% CI : (0.3781, 0.4083)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.1952          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000      0.0000          0.9633
## Specificity                  1.00000       1.00000      1.0000          0.1959
## Pos Pred Value                   NaN           NaN         NaN          0.2968
## Neg Pred Value               0.90294       0.96176      0.8801          0.9381
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.00000       0.00000      0.0000          0.2510
## Detection Prevalence         0.00000       0.00000      0.0000          0.8456
## Balanced Accuracy            0.50000       0.50000      0.5000          0.5796
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.0000      0.61513     0.26076
## Specificity                1.0000      0.99654     0.98845
## Pos Pred Value                NaN      0.96891     0.84426
## Neg Pred Value             0.8583      0.93665     0.84776
## Prevalence                 0.1417      0.14902     0.19363
## Detection Rate             0.0000      0.09167     0.05049
## Detection Prevalence       0.0000      0.09461     0.05980
## Balanced Accuracy          0.5000      0.80584     0.62460
ad_tda_kde_5.40.5_n4_db_svm_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     0    0
##   DERMASON      395    156  489     1024   578   228  580
##   HOROZ           0      0    0        0     0     0    0
##   SEKER           0      0    0        8     0   374    4
##   SIRA            1      0    0       31     0     6  206
## 
## Overall Statistics
##                                           
##                Accuracy : 0.3931          
##                  95% CI : (0.3781, 0.4083)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.1952          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000      0.0000          0.9633
## Specificity                  1.00000       1.00000      1.0000          0.1959
## Pos Pred Value                   NaN           NaN         NaN          0.2968
## Neg Pred Value               0.90294       0.96176      0.8801          0.9381
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.00000       0.00000      0.0000          0.2510
## Detection Prevalence         0.00000       0.00000      0.0000          0.8456
## Balanced Accuracy            0.50000       0.50000      0.5000          0.5796
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.0000      0.61513     0.26076
## Specificity                1.0000      0.99654     0.98845
## Pos Pred Value                NaN      0.96891     0.84426
## Neg Pred Value             0.8583      0.93665     0.84776
## Prevalence                 0.1417      0.14902     0.19363
## Detection Rate             0.0000      0.09167     0.05049
## Detection Prevalence       0.0000      0.09461     0.05980
## Balanced Accuracy          0.5000      0.80584     0.62460
ad_tda_kde_5.40.5_n4_db_svm_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   3.931373e-01   1.951561e-01   3.781080e-01   4.083197e-01   2.605392e-01 
## AccuracyPValue  McnemarPValue 
##   2.291637e-76            NaN
ad_tda_kde_5.40.5_n4_db_svm_cf0_ov_acc<-ad_tda_kde_5.40.5_n4_db_svm_cf0$overall[1]
ad_tda_kde_5.40.5_n4_db_svm_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.0000000   1.0000000            NaN      0.9029412        NA
## Class: BOMBAY     0.0000000   1.0000000            NaN      0.9617647        NA
## Class: CALI       0.0000000   1.0000000            NaN      0.8801471        NA
## Class: DERMASON   0.9633114   0.1958900      0.2968116      0.9380952 0.2968116
## Class: HOROZ      0.0000000   1.0000000            NaN      0.8583333        NA
## Class: SEKER      0.6151316   0.9965438      0.9689119      0.9366540 0.9689119
## Class: SIRA       0.2607595   0.9884498      0.8442623      0.8477581 0.8442623
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.0000000        NA 0.09705882     0.00000000
## Class: BOMBAY   0.0000000        NA 0.03823529     0.00000000
## Class: CALI     0.0000000        NA 0.11985294     0.00000000
## Class: DERMASON 0.9633114 0.4538001 0.26053922     0.25098039
## Class: HOROZ    0.0000000        NA 0.14166667     0.00000000
## Class: SEKER    0.6151316 0.7525151 0.14901961     0.09166667
## Class: SIRA     0.2607595 0.3984526 0.19362745     0.05049020
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA           0.00000000         0.5000000
## Class: BOMBAY             0.00000000         0.5000000
## Class: CALI               0.00000000         0.5000000
## Class: DERMASON           0.84558824         0.5796007
## Class: HOROZ              0.00000000         0.5000000
## Class: SEKER              0.09460784         0.8058377
## Class: SIRA               0.05980392         0.6246047
ad_tda_kde_5.40.5_n4_db_svm_cf0_pre_rec_f1<-ad_tda_kde_5.40.5_n4_db_svm_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_kde_5.40.5_svm_n4_3_fold<-(db_svm_fit_re - ad_tda_kde_5.40.5_n4_svm_fit_re)
diff_drybean_tda_kde_5.40.5_svm_n4_3_fold
##     Accuracy
## 1 0.12446036
## 2 0.09158707
## 3 0.12320733
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_svm.n4_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_svm_n4_3_fold),-0.01,0.01)
bst_tda_kde_5.40.5_svm.n4_3_fold
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_svm.n4_3_fold_odds.left<-bst_tda_kde_5.40.5_svm.n4_3_fold$probLeft/bst_tda_kde_5.40.5_svm.n4_3_fold$probRight
bst_tda_kde_5.40.5_svm.n4_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_svm.n4_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_svm_n4_3_fold),-0.01,0.01)
bsr_tda_kde_5.40.5_svm.n4_3_fold
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.0097
## 
## $winRight
## [1] 0.9903
# Bayesian Correlated Test

bct_tda_kde_5.40.5_svm.n4_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_svm_n4_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_svm.n4_3_fold
## $left
## [1] 0.005013603
## 
## $rope
## [1] 0.002088858
## 
## $right
## [1] 0.9928975
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.40.5_svm_n4_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_kde_5.40.5_svm.n4_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_svm_n4_3_fold))
#bf_tda_kde_5.40.5_svm.n4_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.40.5_svm_n4_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_kde_5.40.5_svm_n4_3_fold)
## t = 10.515, df = 2, p-value = 0.008924
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  0.06680986 0.15935999
## sample estimates:
## mean of x 
## 0.1130849
### Test set diff
diff_drybean_tda_kde_5.40.5_svm.n4_test<-(db_svm_cf_ov_acc-ad_tda_kde_5.40.5_n4_db_svm_cf0_ov_acc)
diff_drybean_tda_kde_5.40.5_svm.n4_test
##  Accuracy 
## 0.5343137
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_svm.n4_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_svm.n4_test),-0.01,0.01)
bst_tda_kde_5.40.5_svm.n4_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_svm.n4_test_odds.left<-bst_tda_kde_5.40.5_svm.n4_test$probLeft/bst_tda_kde_5.40.5_svm.n4_test$probRight
bst_tda_kde_5.40.5_svm.n4_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_svm.n4_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_svm.n4_test),-0.01,0.01)
bsr_tda_kde_5.40.5_svm.n4_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1624333
## 
## $winRight
## [1] 0.8375667
# Bayesian Correlated Test

bct_tda_kde_5.40.5_svm.n4_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_svm.n4_test),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_svm.n4_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_svm.n4_test))

#BayesFactor
#bf_tda_kde_5.40.5_svm.n4_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_svm.n4_test)) #bf_tda_kde_5.40.5_svm.n4_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_svm.n4_test))

##Node5

DryBean_TDA_KDE_5.40.5_n5_SvmFit0 <- train(as.factor(Class) ~ ., data =  tda.m_kde_dry_bean_dataset_5.40.5.n5.vec, 
                    Importance = T,
                    method = 'svmRadial', 
                  trControl = fitControl,
                          tuneGrid = svmGrid, preProc = c('center','scale'),
                          metric='Accuracy')

DryBean_TDA_KDE_5.40.5_n5_SvmFit0
## Support Vector Machines with Radial Basis Function Kernel 
## 
## 774 samples
##  16 predictor
##   4 classes: 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## Pre-processing: centered (16), scaled (16) 
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 516, 515, 517 
## Resampling results across tuning parameters:
## 
##   sigma  C     Accuracy   Kappa     
##    0.1   0.25  0.6460420  0.33122384
##    0.1   0.50  0.6473090  0.34840774
##    0.1   0.75  0.6486010  0.35951461
##    0.1   1.00  0.6499031  0.36756982
##    0.1   1.25  0.6447350  0.36354658
##    1.0   0.25  0.5788022  0.05426709
##    1.0   0.50  0.6137065  0.18698345
##    1.0   0.75  0.6136765  0.21415995
##    1.0   1.00  0.6175626  0.24020761
##    1.0   1.25  0.6304977  0.28880207
##   10.0   0.25  0.5658971  0.00000000
##   10.0   0.50  0.5658971  0.00000000
##   10.0   0.75  0.5658971  0.00000000
##   10.0   1.00  0.5658971  0.00000000
##   10.0   1.25  0.5658971  0.00000000
## 
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.1 and C = 1.
DryBean_TDA_KDE_5.40.5_n5_SvmFit0$resample
##    Accuracy     Kappa Resample
## 1 0.5038760 0.1462475    Fold1
## 2 0.7104247 0.4471954    Fold2
## 3 0.7354086 0.5092665    Fold3
ad_tda_kde_5.40.5_n5_svm_fit_re<-DryBean_TDA_KDE_5.40.5_n5_SvmFit0 $resample[1]

summary(DryBean_TDA_KDE_5.40.5_n5_SvmFit0)
## Length  Class   Mode 
##      1   ksvm     S4
#vip(DryBean_TDA_KDE_5.40.5_n5_SvmFit0,25) + ggtitle("DryBean_TDA_KDE_5.40.5_n5_SvmFit TDA-Assited Svm")

# Predict outcome using DryBean_TDA_KDE_5.40.5_n5_SvmFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.40.5_n5_SvmFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
ad_tda_kde_5.40.5_n5_db_svm_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length

## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
ad_tda_kde_5.40.5_n5_db_svm_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     0    0
##   DERMASON      396    156  489     1049   578   458  756
##   HOROZ           0      0    0        0     0     0    0
##   SEKER           0      0    0        7     0   146    3
##   SIRA            0      0    0        7     0     4   31
## 
## Overall Statistics
##                                           
##                Accuracy : 0.3005          
##                  95% CI : (0.2864, 0.3148)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : 5.635e-09       
##                                           
##                   Kappa : 0.0603          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000      0.0000         0.98683
## Specificity                  1.00000       1.00000      1.0000         0.06099
## Pos Pred Value                   NaN           NaN         NaN         0.27022
## Neg Pred Value               0.90294       0.96176      0.8801         0.92929
## Prevalence                   0.09706       0.03824      0.1199         0.26054
## Detection Rate               0.00000       0.00000      0.0000         0.25711
## Detection Prevalence         0.00000       0.00000      0.0000         0.95147
## Balanced Accuracy            0.50000       0.50000      0.5000         0.52391
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.0000      0.24013    0.039241
## Specificity                1.0000      0.99712    0.996657
## Pos Pred Value                NaN      0.93590    0.738095
## Neg Pred Value             0.8583      0.88226    0.812036
## Prevalence                 0.1417      0.14902    0.193627
## Detection Rate             0.0000      0.03578    0.007598
## Detection Prevalence       0.0000      0.03824    0.010294
## Balanced Accuracy          0.5000      0.61863    0.517949
ad_tda_kde_5.40.5_n5_db_svm_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     0    0
##   DERMASON      396    156  489     1049   578   458  756
##   HOROZ           0      0    0        0     0     0    0
##   SEKER           0      0    0        7     0   146    3
##   SIRA            0      0    0        7     0     4   31
## 
## Overall Statistics
##                                           
##                Accuracy : 0.3005          
##                  95% CI : (0.2864, 0.3148)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : 5.635e-09       
##                                           
##                   Kappa : 0.0603          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000      0.0000         0.98683
## Specificity                  1.00000       1.00000      1.0000         0.06099
## Pos Pred Value                   NaN           NaN         NaN         0.27022
## Neg Pred Value               0.90294       0.96176      0.8801         0.92929
## Prevalence                   0.09706       0.03824      0.1199         0.26054
## Detection Rate               0.00000       0.00000      0.0000         0.25711
## Detection Prevalence         0.00000       0.00000      0.0000         0.95147
## Balanced Accuracy            0.50000       0.50000      0.5000         0.52391
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.0000      0.24013    0.039241
## Specificity                1.0000      0.99712    0.996657
## Pos Pred Value                NaN      0.93590    0.738095
## Neg Pred Value             0.8583      0.88226    0.812036
## Prevalence                 0.1417      0.14902    0.193627
## Detection Rate             0.0000      0.03578    0.007598
## Detection Prevalence       0.0000      0.03824    0.010294
## Balanced Accuracy          0.5000      0.61863    0.517949
ad_tda_kde_5.40.5_n5_db_svm_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   3.004902e-01   6.032099e-02   2.864467e-01   3.148199e-01   2.605392e-01 
## AccuracyPValue  McnemarPValue 
##   5.635067e-09            NaN
ad_tda_kde_5.40.5_n5_db_svm_cf0_ov_acc<-ad_tda_kde_5.40.5_n5_db_svm_cf0$overall[1]
ad_tda_kde_5.40.5_n5_db_svm_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA  0.00000000  1.00000000            NaN      0.9029412        NA
## Class: BOMBAY    0.00000000  1.00000000            NaN      0.9617647        NA
## Class: CALI      0.00000000  1.00000000            NaN      0.8801471        NA
## Class: DERMASON  0.98682973  0.06098774      0.2702215      0.9292929 0.2702215
## Class: HOROZ     0.00000000  1.00000000            NaN      0.8583333        NA
## Class: SEKER     0.24013158  0.99711982      0.9358974      0.8822630 0.9358974
## Class: SIRA      0.03924051  0.99665653      0.7380952      0.8120357 0.7380952
##                     Recall         F1 Prevalence Detection Rate
## Class: BARBUNYA 0.00000000         NA 0.09705882    0.000000000
## Class: BOMBAY   0.00000000         NA 0.03823529    0.000000000
## Class: CALI     0.00000000         NA 0.11985294    0.000000000
## Class: DERMASON 0.98682973 0.42426694 0.26053922    0.257107843
## Class: HOROZ    0.00000000         NA 0.14166667    0.000000000
## Class: SEKER    0.24013158 0.38219895 0.14901961    0.035784314
## Class: SIRA     0.03924051 0.07451923 0.19362745    0.007598039
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA           0.00000000         0.5000000
## Class: BOMBAY             0.00000000         0.5000000
## Class: CALI               0.00000000         0.5000000
## Class: DERMASON           0.95147059         0.5239087
## Class: HOROZ              0.00000000         0.5000000
## Class: SEKER              0.03823529         0.6186257
## Class: SIRA               0.01029412         0.5179485
ad_tda_kde_5.40.5_n5_db_svm_cf0_pre_rec_f1<-ad_tda_kde_5.40.5_n5_db_svm_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_kde_5.40.5_svm_n5_3_fold<-(db_svm_fit_re - ad_tda_kde_5.40.5_n5_svm_fit_re)
diff_drybean_tda_kde_5.40.5_svm_n5_3_fold
##    Accuracy
## 1 0.4246730
## 2 0.2190463
## 3 0.2000855
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_svm.n5_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_svm_n5_3_fold),-0.01,0.01)
bst_tda_kde_5.40.5_svm.n5_3_fold
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_svm.n5_3_fold_odds.left<-bst_tda_kde_5.40.5_svm.n5_3_fold$probLeft/bst_tda_kde_5.40.5_svm.n5_3_fold$probRight
bst_tda_kde_5.40.5_svm.n5_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_svm.n5_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_svm_n5_3_fold),-0.01,0.01)
bsr_tda_kde_5.40.5_svm.n5_3_fold
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.008633333
## 
## $winRight
## [1] 0.9913667
# Bayesian Correlated Test

bct_tda_kde_5.40.5_svm.n5_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_svm_n5_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_svm.n5_3_fold
## $left
## [1] 0.03627002
## 
## $rope
## [1] 0.00487849
## 
## $right
## [1] 0.9588515
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.40.5_svm_n5_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_kde_5.40.5_svm.n5_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_svm_n5_3_fold))
#bf_tda_kde_5.40.5_svm.n5_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.40.5_svm_n5_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_kde_5.40.5_svm_n5_3_fold)
## t = 3.9113, df = 2, p-value = 0.05958
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.0281397  0.5906762
## sample estimates:
## mean of x 
## 0.2812683
### Test set diff
diff_drybean_tda_kde_5.40.5_svm.n5_test<-(db_svm_cf_ov_acc-ad_tda_kde_5.40.5_n5_db_svm_cf0_ov_acc)
diff_drybean_tda_kde_5.40.5_svm.n5_test
##  Accuracy 
## 0.6269608
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_svm.n5_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_svm.n5_test),-0.01,0.01)
bst_tda_kde_5.40.5_svm.n5_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_svm.n5_test_odds.left<-bst_tda_kde_5.40.5_svm.n5_test$probLeft/bst_tda_kde_5.40.5_svm.n5_test$probRight
bst_tda_kde_5.40.5_svm.n5_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_svm.n5_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_svm.n5_test),-0.01,0.01)
bsr_tda_kde_5.40.5_svm.n5_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1574333
## 
## $winRight
## [1] 0.8425667
# Bayesian Correlated Test

bct_tda_kde_5.40.5_svm.n5_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_svm.n5_test),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_svm.n5_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_svm.n5_test))

#BayesFactor
#bf_tda_kde_5.40.5_svm.n5_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_svm.n5_test)) #bf_tda_kde_5.40.5_svm.n5_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_svm.n5_test))


#Non-TDA-Assisted

nn1Grid<-expand.grid(size = c(2,3,5,7), decay = c(0.3,0.5,0.7))
#Neural Network 
dryBeanNn1Fit <- train(as.factor(Class) ~ ., data = Dry_Bean_DatasetTrain, 
                            Importance = T,
                                    method = 'nnet', 
                                    trControl = fitControl,
                                    tuneGrid = nn1Grid,
                                                    metric='Accuracy')
## # weights:  55
## initial  value 14210.828654 
## iter  10 value 11687.640603
## iter  20 value 11657.732161
## iter  30 value 11646.317974
## iter  40 value 11375.572009
## iter  50 value 10186.662906
## iter  60 value 9559.659419
## iter  70 value 9012.444143
## iter  80 value 7799.703709
## iter  90 value 7136.826987
## iter 100 value 6579.957270
## final  value 6579.957270 
## stopped after 100 iterations
## # weights:  79
## initial  value 15119.880886 
## iter  10 value 11657.216869
## final  value 11657.198644 
## converged
## # weights:  127
## initial  value 14711.069274 
## iter  10 value 11657.197444
## iter  20 value 11015.686697
## iter  30 value 10714.220673
## iter  40 value 9397.513400
## iter  50 value 9229.604739
## iter  60 value 8293.950931
## iter  70 value 7620.145089
## iter  80 value 6747.502403
## iter  90 value 6634.694108
## iter 100 value 6427.466188
## final  value 6427.466188 
## stopped after 100 iterations
## # weights:  175
## initial  value 13448.798183 
## iter  10 value 11657.211009
## final  value 11657.198756 
## converged
## # weights:  55
## initial  value 13149.943071 
## iter  10 value 11662.165291
## iter  20 value 11639.003795
## iter  30 value 10675.130086
## iter  40 value 9099.870910
## iter  50 value 8706.325979
## iter  60 value 8603.323444
## iter  70 value 8444.787267
## iter  80 value 7429.823629
## iter  90 value 7000.604721
## iter 100 value 6662.745633
## final  value 6662.745633 
## stopped after 100 iterations
## # weights:  79
## initial  value 12768.865642 
## iter  10 value 11659.683150
## iter  20 value 11657.325520
## iter  30 value 11214.042394
## iter  40 value 10248.338936
## iter  50 value 9795.233799
## iter  60 value 9511.806399
## iter  70 value 9256.215266
## iter  80 value 8962.606629
## iter  90 value 8716.317545
## iter 100 value 8582.496118
## final  value 8582.496118 
## stopped after 100 iterations
## # weights:  127
## initial  value 19663.921409 
## iter  10 value 12191.612110
## iter  20 value 11747.319246
## iter  30 value 11657.532134
## iter  40 value 11522.483498
## iter  50 value 10525.499094
## iter  60 value 10248.441360
## iter  70 value 9908.262706
## iter  80 value 9223.496229
## iter  90 value 8041.905688
## iter 100 value 6678.104238
## final  value 6678.104238 
## stopped after 100 iterations
## # weights:  175
## initial  value 12571.831542 
## iter  10 value 11683.360538
## iter  20 value 11657.532794
## iter  30 value 11657.257618
## final  value 11657.254601 
## converged
## # weights:  55
## initial  value 12725.444452 
## iter  10 value 11812.369849
## iter  20 value 11658.053268
## iter  30 value 10245.993778
## iter  40 value 10057.173475
## iter  50 value 9864.057206
## iter  60 value 9798.228104
## iter  70 value 8964.128750
## iter  80 value 8626.932436
## iter  90 value 8478.135312
## iter 100 value 8315.634962
## final  value 8315.634962 
## stopped after 100 iterations
## # weights:  79
## initial  value 15717.785001 
## iter  10 value 11657.817932
## final  value 11657.366098 
## converged
## # weights:  127
## initial  value 14150.808234 
## iter  10 value 11658.141192
## final  value 11657.496153 
## converged
## # weights:  175
## initial  value 13159.042532 
## iter  10 value 11710.785339
## iter  20 value 11657.673193
## iter  30 value 11621.113149
## iter  40 value 11565.965473
## iter  50 value 11429.938966
## iter  60 value 8335.494823
## iter  70 value 7850.002568
## iter  80 value 7334.367154
## iter  90 value 6383.516023
## iter 100 value 5605.559707
## final  value 5605.559707 
## stopped after 100 iterations
## # weights:  55
## initial  value 11946.764974 
## iter  10 value 11657.441061
## final  value 11657.436618 
## converged
## # weights:  79
## initial  value 12445.546762 
## iter  10 value 11658.398912
## iter  20 value 11538.715278
## iter  30 value 10893.067021
## iter  40 value 10704.789587
## iter  50 value 10668.262528
## iter  60 value 10666.029164
## iter  70 value 10665.257537
## final  value 10665.227772 
## converged
## # weights:  127
## initial  value 14120.483380 
## iter  10 value 11697.667920
## iter  20 value 11620.604810
## iter  30 value 10108.092906
## iter  40 value 9253.154893
## iter  50 value 9079.651804
## iter  60 value 7861.564426
## iter  70 value 7562.803942
## iter  80 value 7415.785398
## iter  90 value 7353.316344
## iter 100 value 7206.350346
## final  value 7206.350346 
## stopped after 100 iterations
## # weights:  175
## initial  value 12563.178623 
## iter  10 value 11659.273994
## iter  20 value 11637.226252
## iter  30 value 11150.002507
## iter  40 value 10537.569723
## iter  50 value 9015.373210
## iter  60 value 7863.290313
## iter  70 value 7167.843850
## iter  80 value 6874.408296
## iter  90 value 6817.225668
## iter 100 value 6732.355579
## final  value 6732.355579 
## stopped after 100 iterations
## # weights:  55
## initial  value 14250.998247 
## iter  10 value 11700.821122
## iter  20 value 11657.997308
## iter  30 value 11651.614706
## iter  40 value 10996.562097
## iter  50 value 9867.221575
## iter  60 value 9253.853948
## iter  70 value 8848.809990
## iter  80 value 8409.357836
## iter  90 value 8079.778713
## iter 100 value 7902.659632
## final  value 7902.659632 
## stopped after 100 iterations
## # weights:  79
## initial  value 13000.533718 
## iter  10 value 11664.099276
## iter  20 value 11657.227152
## iter  30 value 11657.140248
## final  value 11657.139336 
## converged
## # weights:  127
## initial  value 12800.922318 
## iter  10 value 11659.262242
## iter  20 value 11657.163839
## final  value 11657.139289 
## converged
## # weights:  175
## initial  value 13029.270994 
## iter  10 value 11658.542353
## iter  20 value 11654.646638
## iter  30 value 9759.245025
## iter  40 value 9499.281228
## iter  50 value 9415.893963
## iter  60 value 9398.204523
## iter  70 value 9159.923070
## iter  80 value 9038.052893
## iter  90 value 8961.231727
## iter 100 value 8791.871675
## final  value 8791.871675 
## stopped after 100 iterations
## # weights:  55
## initial  value 13604.121340 
## iter  10 value 11658.997570
## iter  20 value 11421.663361
## iter  30 value 10957.611692
## iter  40 value 10790.460139
## iter  50 value 10785.965210
## iter  60 value 10785.257780
## iter  70 value 10778.244866
## iter  80 value 10692.679453
## iter  90 value 9996.846365
## iter 100 value 8490.319966
## final  value 8490.319966 
## stopped after 100 iterations
## # weights:  79
## initial  value 13083.923193 
## iter  10 value 11657.454954
## iter  20 value 11577.567657
## iter  30 value 11457.258722
## iter  40 value 9484.689539
## iter  50 value 8870.928455
## iter  60 value 8703.472637
## iter  70 value 7930.617282
## iter  80 value 7180.805614
## iter  90 value 6887.983113
## iter 100 value 6672.383509
## final  value 6672.383509 
## stopped after 100 iterations
## # weights:  127
## initial  value 12876.807894 
## iter  10 value 11657.313520
## iter  20 value 11657.288254
## iter  20 value 11657.288216
## final  value 11657.287962 
## converged
## # weights:  175
## initial  value 15234.643067 
## iter  10 value 11741.631888
## iter  20 value 11657.079024
## iter  30 value 11644.451583
## iter  40 value 11617.131559
## iter  50 value 10333.101258
## iter  60 value 9603.035273
## iter  70 value 9400.753319
## iter  80 value 9165.383548
## iter  90 value 8692.277556
## iter 100 value 8227.296155
## final  value 8227.296155 
## stopped after 100 iterations
## # weights:  55
## initial  value 12470.157876 
## iter  10 value 11656.827505
## final  value 11656.805402 
## converged
## # weights:  79
## initial  value 14684.828806 
## iter  10 value 11660.194728
## iter  20 value 11582.493109
## iter  30 value 9437.949387
## iter  40 value 9020.819722
## iter  50 value 8983.275394
## iter  60 value 8734.339963
## iter  70 value 6652.088743
## iter  80 value 5821.061365
## iter  90 value 5503.667722
## iter 100 value 5316.924556
## final  value 5316.924556 
## stopped after 100 iterations
## # weights:  127
## initial  value 16443.484429 
## iter  10 value 11858.832414
## iter  20 value 11662.990931
## iter  30 value 11662.950953
## iter  40 value 11657.149598
## iter  40 value 11657.149553
## iter  50 value 11656.806923
## final  value 11656.805157 
## converged
## # weights:  175
## initial  value 13646.883277 
## iter  10 value 11656.626815
## final  value 11656.604473 
## converged
## # weights:  55
## initial  value 12583.781733 
## iter  10 value 11657.602742
## iter  20 value 11656.992843
## final  value 11656.842945 
## converged
## # weights:  79
## initial  value 12788.438246 
## iter  10 value 11687.084380
## iter  20 value 11657.396732
## iter  30 value 10360.547562
## iter  40 value 9249.882658
## iter  50 value 8940.459352
## iter  60 value 8813.841160
## iter  70 value 8778.913337
## iter  80 value 8559.526695
## iter  90 value 8401.205497
## iter 100 value 8147.647296
## final  value 8147.647296 
## stopped after 100 iterations
## # weights:  127
## initial  value 14595.531832 
## iter  10 value 11697.530886
## iter  20 value 11657.183503
## iter  30 value 11656.775113
## iter  40 value 9999.131082
## iter  50 value 9079.168972
## iter  60 value 8816.704741
## iter  70 value 8393.179783
## iter  80 value 7919.892315
## iter  90 value 6967.004244
## iter 100 value 6785.921243
## final  value 6785.921243 
## stopped after 100 iterations
## # weights:  175
## initial  value 12669.518063 
## iter  10 value 11674.246441
## iter  20 value 11657.090914
## iter  30 value 11656.845520
## iter  40 value 11656.816149
## iter  50 value 11627.476404
## iter  60 value 11013.758170
## iter  70 value 10836.109986
## iter  80 value 10446.576824
## iter  90 value 8670.551660
## iter 100 value 6911.262290
## final  value 6911.262290 
## stopped after 100 iterations
## # weights:  55
## initial  value 14375.341551 
## iter  10 value 11659.363865
## final  value 11657.251181 
## converged
## # weights:  79
## initial  value 12718.331545 
## iter  10 value 11657.648444
## final  value 11656.991331 
## converged
## # weights:  127
## initial  value 13883.126858 
## iter  10 value 11657.073575
## iter  20 value 11618.488665
## iter  30 value 10668.903831
## iter  40 value 10101.044447
## iter  50 value 9602.824120
## iter  60 value 9184.929871
## iter  70 value 8877.991645
## iter  80 value 8786.815337
## iter  90 value 8698.249804
## iter 100 value 8507.447843
## final  value 8507.447843 
## stopped after 100 iterations
## # weights:  175
## initial  value 13801.812567 
## iter  10 value 11657.706616
## final  value 11656.731543 
## converged
## # weights:  175
## initial  value 21531.146687 
## iter  10 value 17483.938823
## iter  20 value 17440.993898
## iter  30 value 17427.654762
## iter  40 value 16559.153582
## iter  50 value 16151.485652
## iter  60 value 14348.270303
## iter  70 value 13686.442709
## iter  80 value 13621.214815
## iter  90 value 13534.089337
## iter 100 value 13139.638974
## final  value 13139.638974 
## stopped after 100 iterations
dryBeanNn1Fit
## Neural Network 
## 
## 9531 samples
##   16 predictor
##    7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 6354, 6354, 6354 
## Resampling results across tuning parameters:
## 
##   size  decay  Accuracy   Kappa     
##   2     0.3    0.3887315  0.18707039
##   2     0.5    0.3807575  0.20300069
##   2     0.7    0.3593537  0.16482011
##   3     0.3    0.4001679  0.20874650
##   3     0.5    0.3500157  0.15375512
##   3     0.7    0.3351170  0.11666548
##   5     0.3    0.4382541  0.27319919
##   5     0.5    0.4612318  0.31211609
##   5     0.7    0.3055293  0.07662268
##   7     0.3    0.3483370  0.13751039
##   7     0.5    0.3978596  0.21418166
##   7     0.7    0.4913440  0.34226942
## 
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 7 and decay = 0.7.
dryBeanNn1Fit$resample
##    Accuracy     Kappa Resample
## 1 0.4743469 0.3466286    Fold2
## 2 0.7393768 0.6801796    Fold1
## 3 0.2603085 0.0000000    Fold3
db_nn1_fit_re<-dryBeanNn1Fit$resample[1]

summary(dryBeanNn1Fit)
## a 16-7-7 network with 175 weights
## options were - softmax modelling  decay=0.7
##   b->h1  i1->h1  i2->h1  i3->h1  i4->h1  i5->h1  i6->h1  i7->h1  i8->h1  i9->h1 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
## i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1 i16->h1 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h2  i1->h2  i2->h2  i3->h2  i4->h2  i5->h2  i6->h2  i7->h2  i8->h2  i9->h2 
##    0.00    0.10    0.00    0.00    0.00    0.00    0.00    0.11    0.00    0.00 
## i10->h2 i11->h2 i12->h2 i13->h2 i14->h2 i15->h2 i16->h2 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h3  i1->h3  i2->h3  i3->h3  i4->h3  i5->h3  i6->h3  i7->h3  i8->h3  i9->h3 
##    0.00    0.11    0.00    0.00    0.00    0.00    0.00    0.11    0.00    0.00 
## i10->h3 i11->h3 i12->h3 i13->h3 i14->h3 i15->h3 i16->h3 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h4  i1->h4  i2->h4  i3->h4  i4->h4  i5->h4  i6->h4  i7->h4  i8->h4  i9->h4 
##    0.00    0.01    0.00    0.00    0.00    0.00    0.00    0.01    0.00    0.00 
## i10->h4 i11->h4 i12->h4 i13->h4 i14->h4 i15->h4 i16->h4 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h5  i1->h5  i2->h5  i3->h5  i4->h5  i5->h5  i6->h5  i7->h5  i8->h5  i9->h5 
##    0.10    0.27    1.76    2.80    2.29    0.16   -0.02   -0.33    0.59   -0.08 
## i10->h5 i11->h5 i12->h5 i13->h5 i14->h5 i15->h5 i16->h5 
##    0.10    0.09    0.10    0.00    0.00    0.12    0.10 
##   b->h6  i1->h6  i2->h6  i3->h6  i4->h6  i5->h6  i6->h6  i7->h6  i8->h6  i9->h6 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
## i10->h6 i11->h6 i12->h6 i13->h6 i14->h6 i15->h6 i16->h6 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h7  i1->h7  i2->h7  i3->h7  i4->h7  i5->h7  i6->h7  i7->h7  i8->h7  i9->h7 
##    0.00    0.04    0.00    0.00    0.00    0.00    0.00    0.04    0.00    0.00 
## i10->h7 i11->h7 i12->h7 i13->h7 i14->h7 i15->h7 i16->h7 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##  b->o1 h1->o1 h2->o1 h3->o1 h4->o1 h5->o1 h6->o1 h7->o1 
##   0.37   0.37   0.37   0.18   0.03  -3.30   0.37   0.37 
##  b->o2 h1->o2 h2->o2 h3->o2 h4->o2 h5->o2 h6->o2 h7->o2 
##   0.19   0.18   0.19  -0.04   0.17  -4.76   0.19   0.19 
##  b->o3 h1->o3 h2->o3 h3->o3 h4->o3 h5->o3 h6->o3 h7->o3 
##   0.43   0.43   0.43  -0.02   0.07  -4.18   0.43   0.43 
##  b->o4 h1->o4 h2->o4 h3->o4 h4->o4 h5->o4 h6->o4 h7->o4 
##  -0.20  -0.20  -0.20  -0.07  -0.14   3.31  -0.20  -0.20 
##  b->o5 h1->o5 h2->o5 h3->o5 h4->o5 h5->o5 h6->o5 h7->o5 
##   0.12   0.13   0.12  -0.06   0.03   0.72   0.12   0.12 
##  b->o6 h1->o6 h2->o6 h3->o6 h4->o6 h5->o6 h6->o6 h7->o6 
##  -0.64  -0.65  -0.64   0.07  -0.05   4.93  -0.64  -0.64 
##  b->o7 h1->o7 h2->o7 h3->o7 h4->o7 h5->o7 h6->o7 h7->o7 
##  -0.26  -0.26  -0.26  -0.05  -0.11   3.27  -0.26  -0.26
#vip(dryBeanNn1Fit,25) + ggtitle("non-TDA-Assited NN")

# Predict outcome using model from training data based on testing data
predictions <- predict(dryBeanNn1Fit, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
db_nn1_cf<-confusionMatrix(data=predictions, as.factor(Dry_Bean_DatasetTest$Class))
db_nn1_cf
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI          363    156  479        0   124     5    7
##   DERMASON       33      0    9     1063   454   603  783
##   HOROZ           0      0    1        0     0     0    0
##   SEKER           0      0    0        0     0     0    0
##   SIRA            0      0    0        0     0     0    0
## 
## Overall Statistics
##                                         
##                Accuracy : 0.3779        
##                  95% CI : (0.363, 0.393)
##     No Information Rate : 0.2605        
##     P-Value [Acc > NIR] : < 2.2e-16     
##                                         
##                   Kappa : 0.201         
##                                         
##  Mcnemar's Test P-Value : NA            
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000      0.9796          1.0000
## Specificity                  1.00000       1.00000      0.8176          0.3762
## Pos Pred Value                   NaN           NaN      0.4224          0.3610
## Neg Pred Value               0.90294       0.96176      0.9966          1.0000
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.00000       0.00000      0.1174          0.2605
## Detection Prevalence         0.00000       0.00000      0.2779          0.7218
## Balanced Accuracy            0.50000       0.50000      0.8986          0.6881
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity             0.0000000        0.000      0.0000
## Specificity             0.9997144        1.000      1.0000
## Pos Pred Value          0.0000000          NaN         NaN
## Neg Pred Value          0.8582986        0.851      0.8064
## Prevalence              0.1416667        0.149      0.1936
## Detection Rate          0.0000000        0.000      0.0000
## Detection Prevalence    0.0002451        0.000      0.0000
## Balanced Accuracy       0.4998572        0.500      0.5000
db_nn1_cf$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   3.779412e-01   2.010469e-01   3.630324e-01   3.930249e-01   2.605392e-01 
## AccuracyPValue  McnemarPValue 
##   8.628767e-61            NaN
db_nn1_cf_ov_acc<-db_nn1_cf$overall[1]
db_nn1_cf$byClass 
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.0000000   1.0000000            NaN      0.9029412        NA
## Class: BOMBAY     0.0000000   1.0000000            NaN      0.9617647        NA
## Class: CALI       0.9795501   0.8175996      0.4223986      0.9966056 0.4223986
## Class: DERMASON   1.0000000   0.3762015      0.3609508      1.0000000 0.3609508
## Class: HOROZ      0.0000000   0.9997144      0.0000000      0.8582986 0.0000000
## Class: SEKER      0.0000000   1.0000000            NaN      0.8509804        NA
## Class: SIRA       0.0000000   1.0000000            NaN      0.8063725        NA
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.0000000        NA 0.09705882      0.0000000
## Class: BOMBAY   0.0000000        NA 0.03823529      0.0000000
## Class: CALI     0.9795501 0.5902649 0.11985294      0.1174020
## Class: DERMASON 1.0000000 0.5304391 0.26053922      0.2605392
## Class: HOROZ    0.0000000       NaN 0.14166667      0.0000000
## Class: SEKER    0.0000000        NA 0.14901961      0.0000000
## Class: SIRA     0.0000000        NA 0.19362745      0.0000000
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA          0.000000000         0.5000000
## Class: BOMBAY            0.000000000         0.5000000
## Class: CALI              0.277941176         0.8985748
## Class: DERMASON          0.721813725         0.6881008
## Class: HOROZ             0.000245098         0.4998572
## Class: SEKER             0.000000000         0.5000000
## Class: SIRA              0.000000000         0.5000000
db_nn1_cf_pre_rec_f1<-db_nn1_cf$byClass[5:7]

##With TDA PCA filter 5 intervals, 50% overlap, 5 bins 
##Node1

#Neural Network 1
DryBean_TDA_PC_5.40.5_n1_NN1Fit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.40.5.n1.vec, 
                           Importance = T,
                     method = 'nnet', 
                     trControl = fitControl,
                     tuneGrid = nn1Grid,
                     metric='Accuracy')
## # weights:  52
## initial  value 10481.590998 
## iter  10 value 4868.892369
## iter  20 value 4757.544783
## iter  30 value 4747.910703
## iter  40 value 4747.472502
## iter  50 value 4747.370322
## final  value 4747.367190 
## converged
## # weights:  75
## initial  value 7552.442234 
## iter  10 value 4775.490567
## iter  20 value 4755.116939
## iter  30 value 4754.233899
## iter  40 value 4093.499824
## iter  50 value 3084.223614
## iter  60 value 2963.190982
## iter  70 value 2811.743319
## iter  80 value 2117.494541
## iter  90 value 1549.203824
## iter 100 value 1456.169777
## final  value 1456.169777 
## stopped after 100 iterations
## # weights:  121
## initial  value 10927.288974 
## iter  10 value 4935.624860
## iter  20 value 4766.730109
## iter  30 value 4748.411063
## iter  40 value 4300.483608
## iter  50 value 3324.802641
## iter  60 value 2657.035603
## iter  70 value 2567.809420
## iter  80 value 2505.076530
## iter  90 value 2374.275292
## iter 100 value 2346.867240
## final  value 2346.867240 
## stopped after 100 iterations
## # weights:  167
## initial  value 10068.943367 
## iter  10 value 4895.720822
## iter  20 value 4311.781896
## iter  30 value 4005.335733
## iter  40 value 3972.027375
## iter  50 value 3526.192050
## iter  60 value 3340.972388
## iter  70 value 3099.746804
## iter  80 value 2615.419915
## iter  90 value 2305.145454
## iter 100 value 2170.297178
## final  value 2170.297178 
## stopped after 100 iterations
## # weights:  52
## initial  value 7306.851174 
## iter  10 value 4772.880097
## iter  20 value 4759.346485
## iter  30 value 4751.754890
## iter  40 value 4750.646855
## iter  50 value 4750.539098
## iter  60 value 4750.497703
## iter  70 value 3680.506704
## iter  80 value 2976.373987
## iter  90 value 2725.349547
## iter 100 value 2637.039164
## final  value 2637.039164 
## stopped after 100 iterations
## # weights:  75
## initial  value 7790.057840 
## iter  10 value 4888.285678
## iter  20 value 4754.197881
## iter  30 value 2840.918348
## iter  40 value 2430.885896
## iter  50 value 2215.539502
## iter  60 value 1845.604384
## iter  70 value 1739.287391
## iter  80 value 1671.827057
## iter  90 value 1530.613437
## iter 100 value 1294.620521
## final  value 1294.620521 
## stopped after 100 iterations
## # weights:  121
## initial  value 14976.716729 
## iter  10 value 4867.738017
## iter  20 value 4758.994476
## iter  30 value 4754.982095
## iter  40 value 4706.174307
## iter  50 value 3723.866012
## iter  60 value 2884.196015
## iter  70 value 2779.797669
## iter  80 value 2667.952025
## iter  90 value 2508.794016
## iter 100 value 2377.841554
## final  value 2377.841554 
## stopped after 100 iterations
## # weights:  167
## initial  value 7810.424184 
## iter  10 value 4754.892924
## iter  20 value 4747.965878
## iter  30 value 4746.590126
## iter  40 value 4644.112532
## iter  50 value 4450.567130
## iter  60 value 3378.146557
## iter  70 value 2928.380963
## iter  80 value 2644.698515
## iter  90 value 2566.152515
## iter 100 value 2209.508344
## final  value 2209.508344 
## stopped after 100 iterations
## # weights:  52
## initial  value 8062.451443 
## iter  10 value 4797.355106
## iter  20 value 4762.019674
## iter  30 value 4755.232699
## iter  40 value 4754.119877
## iter  50 value 4236.353990
## iter  60 value 3897.967100
## iter  70 value 3503.904199
## iter  80 value 2746.578236
## iter  90 value 2596.409279
## iter 100 value 2551.583086
## final  value 2551.583086 
## stopped after 100 iterations
## # weights:  75
## initial  value 8385.094880 
## iter  10 value 4799.208667
## iter  20 value 4759.742085
## iter  30 value 4758.045552
## iter  40 value 4751.353206
## iter  50 value 4666.127571
## iter  60 value 4626.334474
## iter  70 value 4610.397528
## iter  80 value 4363.846866
## iter  90 value 3736.476561
## iter 100 value 3318.436695
## final  value 3318.436695 
## stopped after 100 iterations
## # weights:  121
## initial  value 9533.636681 
## iter  10 value 4775.170381
## iter  20 value 4755.389642
## iter  30 value 4732.756538
## iter  40 value 3278.801743
## iter  50 value 2892.475343
## iter  60 value 2755.141916
## iter  70 value 2551.275877
## iter  80 value 2511.440453
## iter  90 value 2374.116921
## iter 100 value 2122.672184
## final  value 2122.672184 
## stopped after 100 iterations
## # weights:  167
## initial  value 7514.501257 
## iter  10 value 4807.748224
## iter  20 value 4754.846322
## iter  30 value 4751.015782
## iter  40 value 4750.424181
## iter  50 value 4734.946179
## iter  60 value 4665.461270
## iter  70 value 4261.804995
## iter  80 value 3456.386062
## iter  90 value 3306.200710
## iter 100 value 2965.206631
## final  value 2965.206631 
## stopped after 100 iterations
## # weights:  52
## initial  value 7221.842650 
## iter  10 value 4776.306042
## iter  20 value 4758.265306
## iter  30 value 3915.469686
## iter  40 value 3592.958724
## iter  50 value 3367.529675
## iter  60 value 3166.222497
## iter  70 value 3037.588037
## iter  80 value 2845.678738
## iter  90 value 2440.729839
## iter 100 value 2018.730402
## final  value 2018.730402 
## stopped after 100 iterations
## # weights:  75
## initial  value 9901.662567 
## iter  10 value 4855.849589
## iter  20 value 4760.814546
## iter  30 value 4752.485412
## iter  40 value 4747.675210
## iter  50 value 4370.374112
## iter  60 value 3346.061842
## iter  70 value 2995.732135
## iter  80 value 2576.446969
## iter  90 value 2439.061530
## iter 100 value 1774.896658
## final  value 1774.896658 
## stopped after 100 iterations
## # weights:  121
## initial  value 8553.250164 
## iter  10 value 4765.256310
## iter  20 value 4758.206326
## iter  30 value 4754.406774
## iter  40 value 4016.637944
## iter  50 value 3477.543030
## iter  60 value 3412.304197
## iter  70 value 3386.124891
## iter  80 value 3265.901626
## iter  90 value 2781.749040
## iter 100 value 2692.603444
## final  value 2692.603444 
## stopped after 100 iterations
## # weights:  167
## initial  value 8121.827649 
## iter  10 value 4787.182988
## iter  20 value 4755.508445
## iter  30 value 4750.212651
## iter  40 value 4738.410209
## iter  50 value 3260.321647
## iter  60 value 2874.060760
## iter  70 value 2697.337010
## iter  80 value 2284.823053
## iter  90 value 2115.173037
## iter 100 value 1822.497791
## final  value 1822.497791 
## stopped after 100 iterations
## # weights:  52
## initial  value 7561.854942 
## iter  10 value 5052.385612
## iter  20 value 4785.511978
## iter  30 value 4760.779779
## iter  40 value 4759.423268
## final  value 4759.051386 
## converged
## # weights:  75
## initial  value 9687.965478 
## iter  10 value 4918.878227
## iter  20 value 4766.599426
## iter  30 value 4742.276899
## iter  40 value 4263.073489
## iter  50 value 2643.611670
## iter  60 value 1969.780214
## iter  70 value 1869.585602
## iter  80 value 1695.773685
## iter  90 value 1609.368787
## iter 100 value 1401.197232
## final  value 1401.197232 
## stopped after 100 iterations
## # weights:  121
## initial  value 8978.942344 
## iter  10 value 4931.990328
## iter  20 value 4769.987882
## iter  30 value 4755.870445
## iter  40 value 4754.746034
## iter  50 value 4749.074983
## iter  60 value 4737.948816
## iter  70 value 3808.054550
## iter  80 value 3528.337038
## iter  90 value 3319.130260
## iter 100 value 2596.735043
## final  value 2596.735043 
## stopped after 100 iterations
## # weights:  167
## initial  value 8247.541818 
## iter  10 value 4863.375800
## iter  20 value 4755.726684
## iter  30 value 4752.298274
## iter  40 value 4727.773798
## iter  50 value 4697.645889
## iter  60 value 3741.347805
## iter  70 value 2970.835831
## iter  80 value 2704.546605
## iter  90 value 2092.195095
## iter 100 value 2016.460206
## final  value 2016.460206 
## stopped after 100 iterations
## # weights:  52
## initial  value 7495.104766 
## iter  10 value 4782.576577
## iter  20 value 4771.515463
## iter  30 value 4448.600541
## iter  40 value 3760.793025
## iter  50 value 3586.453048
## iter  60 value 3362.241995
## iter  70 value 3037.561446
## iter  80 value 2844.297989
## iter  90 value 2825.669180
## iter 100 value 2724.706939
## final  value 2724.706939 
## stopped after 100 iterations
## # weights:  75
## initial  value 7381.337950 
## iter  10 value 4799.196605
## iter  20 value 4776.377259
## iter  30 value 3632.643860
## iter  40 value 3306.997832
## iter  50 value 3196.038609
## iter  60 value 2936.991613
## iter  70 value 2791.618834
## iter  80 value 2700.709206
## iter  90 value 2677.358059
## iter 100 value 2354.997895
## final  value 2354.997895 
## stopped after 100 iterations
## # weights:  121
## initial  value 7970.603946 
## iter  10 value 4787.922751
## iter  20 value 4766.898857
## iter  30 value 4761.166986
## iter  40 value 4281.758416
## iter  50 value 4132.539268
## iter  60 value 2688.076874
## iter  70 value 2605.539262
## iter  80 value 2596.973407
## iter  90 value 2517.395628
## iter 100 value 2355.784739
## final  value 2355.784739 
## stopped after 100 iterations
## # weights:  167
## initial  value 7217.952244 
## iter  10 value 4779.072625
## iter  20 value 4762.661476
## iter  30 value 3654.611214
## iter  40 value 3576.312875
## iter  50 value 3574.976688
## iter  60 value 3342.005262
## iter  70 value 2513.741721
## iter  80 value 2261.714104
## iter  90 value 2101.941684
## iter 100 value 1953.870751
## final  value 1953.870751 
## stopped after 100 iterations
## # weights:  52
## initial  value 9562.333761 
## iter  10 value 4866.989164
## iter  20 value 4753.599496
## iter  30 value 4741.859208
## iter  40 value 4740.877328
## final  value 4740.877255 
## converged
## # weights:  75
## initial  value 8097.330657 
## iter  10 value 4764.869492
## iter  20 value 4748.112871
## iter  30 value 4090.515999
## iter  40 value 3670.588680
## iter  50 value 3615.547471
## iter  60 value 3495.865524
## iter  70 value 3227.849779
## iter  80 value 3124.721966
## iter  90 value 3070.521738
## iter 100 value 2930.391504
## final  value 2930.391504 
## stopped after 100 iterations
## # weights:  121
## initial  value 7361.751686 
## iter  10 value 4815.784382
## iter  20 value 4745.561062
## iter  30 value 4739.911675
## iter  40 value 4738.631901
## iter  50 value 4738.474104
## final  value 4738.473439 
## converged
## # weights:  167
## initial  value 9177.150150 
## iter  10 value 4781.411911
## iter  20 value 4780.470257
## iter  30 value 4739.800361
## iter  40 value 4739.141084
## iter  50 value 4738.627612
## iter  60 value 4247.102492
## iter  70 value 3884.304211
## iter  80 value 2949.568374
## iter  90 value 2642.585918
## iter 100 value 2531.050904
## final  value 2531.050904 
## stopped after 100 iterations
## # weights:  52
## initial  value 9198.112021 
## iter  10 value 4872.922707
## iter  20 value 4778.881874
## iter  30 value 3288.668951
## iter  40 value 2896.828917
## iter  50 value 2871.021140
## iter  60 value 2811.454706
## iter  70 value 2488.288665
## iter  80 value 1896.115827
## iter  90 value 1510.859026
## iter 100 value 1422.458627
## final  value 1422.458627 
## stopped after 100 iterations
## # weights:  75
## initial  value 9234.457348 
## iter  10 value 4795.091552
## iter  20 value 4752.728569
## iter  30 value 4745.002158
## iter  40 value 4744.080749
## iter  50 value 4744.006774
## iter  60 value 4743.987034
## iter  70 value 4615.445184
## iter  80 value 3900.308165
## iter  90 value 3687.393616
## iter 100 value 2166.378536
## final  value 2166.378536 
## stopped after 100 iterations
## # weights:  121
## initial  value 11546.243714 
## iter  10 value 4780.671958
## iter  20 value 4740.550753
## iter  30 value 4740.088665
## iter  40 value 4740.079237
## iter  40 value 4740.079196
## iter  40 value 4740.079194
## final  value 4740.079194 
## converged
## # weights:  167
## initial  value 5804.966227 
## iter  10 value 4769.184615
## iter  20 value 4742.940078
## iter  30 value 4740.673541
## iter  40 value 4740.247226
## iter  50 value 4712.326666
## iter  60 value 3892.394570
## iter  70 value 3814.580251
## iter  80 value 3795.612707
## iter  90 value 3792.899801
## iter 100 value 3693.886765
## final  value 3693.886765 
## stopped after 100 iterations
## # weights:  52
## initial  value 9399.338615 
## iter  10 value 4820.000548
## iter  20 value 4754.109794
## iter  30 value 4747.563464
## iter  40 value 4747.147584
## iter  50 value 4747.061968
## final  value 4747.046107 
## converged
## # weights:  75
## initial  value 9581.454544 
## iter  10 value 4774.728684
## iter  20 value 4747.207464
## iter  30 value 4745.350300
## iter  40 value 4273.119561
## iter  50 value 3207.047181
## iter  60 value 2671.009812
## iter  70 value 2578.052538
## iter  80 value 2523.859289
## iter  90 value 2385.981211
## iter 100 value 2342.020481
## final  value 2342.020481 
## stopped after 100 iterations
## # weights:  121
## initial  value 11019.279495 
## iter  10 value 4761.206327
## iter  20 value 4744.347210
## iter  30 value 4743.154486
## iter  40 value 4437.219140
## iter  50 value 3402.603278
## iter  60 value 3271.264431
## iter  70 value 3175.538808
## iter  80 value 2837.014086
## iter  90 value 2576.862299
## iter 100 value 1967.367856
## final  value 1967.367856 
## stopped after 100 iterations
## # weights:  167
## initial  value 10810.525312 
## iter  10 value 4757.188388
## iter  20 value 4745.228289
## iter  30 value 4743.580333
## iter  40 value 4743.367958
## iter  50 value 4742.871627
## iter  60 value 4736.218311
## iter  70 value 3747.120309
## iter  80 value 3563.547989
## iter  90 value 3080.303883
## iter 100 value 2681.434286
## final  value 2681.434286 
## stopped after 100 iterations
## # weights:  75
## initial  value 11177.025315 
## iter  10 value 7280.897393
## iter  20 value 7140.860207
## iter  30 value 7125.684983
## iter  40 value 7124.478281
## iter  50 value 5637.120145
## iter  60 value 4213.676722
## iter  70 value 3813.936334
## iter  80 value 3775.237215
## iter  90 value 3665.148185
## iter 100 value 3618.545923
## final  value 3618.545923 
## stopped after 100 iterations
DryBean_TDA_PC_5.40.5_n1_NN1Fit0
## Neural Network 
## 
## 6835 samples
##   16 predictor
##    6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 4557, 4557, 4556 
## Resampling results across tuning parameters:
## 
##   size  decay  Accuracy   Kappa    
##   2     0.3    0.6158158  0.2395594
##   2     0.5    0.7337004  0.4851405
##   2     0.7    0.6979054  0.4164567
##   3     0.3    0.8324916  0.7188841
##   3     0.5    0.8828130  0.8105818
##   3     0.7    0.7787828  0.6299681
##   5     0.3    0.6968811  0.4109647
##   5     0.5    0.6955641  0.4086257
##   5     0.7    0.8311551  0.7099621
##   7     0.3    0.8336570  0.7092632
##   7     0.5    0.7597864  0.6228876
##   7     0.7    0.8122937  0.6815072
## 
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 3 and decay = 0.5.
DryBean_TDA_PC_5.40.5_n1_NN1Fit0$resample
##    Accuracy     Kappa Resample
## 1 0.8560772 0.7718302    Fold3
## 2 0.8937665 0.8262725    Fold2
## 3 0.8985953 0.8336428    Fold1
db_tda_pc_5.40.5_n1_nn1_fit_re<-DryBean_TDA_PC_5.40.5_n1_NN1Fit0$resample[1]

summary(DryBean_TDA_PC_5.40.5_n1_NN1Fit0)
## a 16-3-6 network with 75 weights
## options were - softmax modelling  decay=0.5
##   b->h1  i1->h1  i2->h1  i3->h1  i4->h1  i5->h1  i6->h1  i7->h1  i8->h1  i9->h1 
##    0.00    0.08    0.00    0.00    0.00    0.00    0.00    0.08    0.00    0.00 
## i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1 i16->h1 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h2  i1->h2  i2->h2  i3->h2  i4->h2  i5->h2  i6->h2  i7->h2  i8->h2  i9->h2 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
## i10->h2 i11->h2 i12->h2 i13->h2 i14->h2 i15->h2 i16->h2 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h3  i1->h3  i2->h3  i3->h3  i4->h3  i5->h3  i6->h3  i7->h3  i8->h3  i9->h3 
##   -0.14    0.01    0.13   -2.13   -2.38   -0.10   -0.05   -0.01    4.00   -0.16 
## i10->h3 i11->h3 i12->h3 i13->h3 i14->h3 i15->h3 i16->h3 
##   -0.14   -0.09   -0.13    0.00    0.00   -0.12   -0.09 
##  b->o1 h1->o1 h2->o1 h3->o1 
##  -0.53  -0.53  -0.53   1.04 
##  b->o2 h1->o2 h2->o2 h3->o2 
##  -1.26  -1.26  -1.26   1.01 
##  b->o3 h1->o3 h2->o3 h3->o3 
##   1.39   1.39   1.39  -3.56 
##  b->o4 h1->o4 h2->o4 h3->o4 
##  -0.48  -0.48  -0.48  -1.00 
##  b->o5 h1->o5 h2->o5 h3->o5 
##  -0.10  -0.10  -0.10   5.03 
##  b->o6 h1->o6 h2->o6 h3->o6 
##   0.97   0.97   0.97  -2.51
#vip(DryBean_TDA_PC_5.40.5_n1_NN1Fit0,25) + ggtitle("dryBean_TDA_PCA_5.40.5_n1_NN1Fit TDA-Assited NN")


# Predict outcome using DryBean_TDA_PC_5.40.5_n1_NN1Fit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.40.5_n1_NN1Fit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.40.5_n1_db_nn1_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length

## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
db_tda_pc_5.40.5_n1_db_nn1_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     0    0
##   DERMASON      129      8  467     1042   578    20  751
##   HOROZ           0      0    0        0     0     0    0
##   SEKER         267    148   22       21     0   588   39
##   SIRA            0      0    0        0     0     0    0
## 
## Overall Statistics
##                                           
##                Accuracy : 0.3995          
##                  95% CI : (0.3844, 0.4147)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.2192          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000      0.0000          0.9802
## Specificity                  1.00000       1.00000      1.0000          0.3527
## Pos Pred Value                   NaN           NaN         NaN          0.3479
## Neg Pred Value               0.90294       0.96176      0.8801          0.9806
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.00000       0.00000      0.0000          0.2554
## Detection Prevalence         0.00000       0.00000      0.0000          0.7341
## Balanced Accuracy            0.50000       0.50000      0.5000          0.6665
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.0000       0.9671      0.0000
## Specificity                1.0000       0.8569      1.0000
## Pos Pred Value                NaN       0.5419         NaN
## Neg Pred Value             0.8583       0.9933      0.8064
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.0000       0.1441      0.0000
## Detection Prevalence       0.0000       0.2659      0.0000
## Balanced Accuracy          0.5000       0.9120      0.5000
db_tda_pc_5.40.5_n1_db_nn1_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     0    0
##   DERMASON      129      8  467     1042   578    20  751
##   HOROZ           0      0    0        0     0     0    0
##   SEKER         267    148   22       21     0   588   39
##   SIRA            0      0    0        0     0     0    0
## 
## Overall Statistics
##                                           
##                Accuracy : 0.3995          
##                  95% CI : (0.3844, 0.4147)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.2192          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000      0.0000          0.9802
## Specificity                  1.00000       1.00000      1.0000          0.3527
## Pos Pred Value                   NaN           NaN         NaN          0.3479
## Neg Pred Value               0.90294       0.96176      0.8801          0.9806
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.00000       0.00000      0.0000          0.2554
## Detection Prevalence         0.00000       0.00000      0.0000          0.7341
## Balanced Accuracy            0.50000       0.50000      0.5000          0.6665
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.0000       0.9671      0.0000
## Specificity                1.0000       0.8569      1.0000
## Pos Pred Value                NaN       0.5419         NaN
## Neg Pred Value             0.8583       0.9933      0.8064
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.0000       0.1441      0.0000
## Detection Prevalence       0.0000       0.2659      0.0000
## Balanced Accuracy          0.5000       0.9120      0.5000
db_tda_pc_5.40.5_n1_db_nn1_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   3.995098e-01   2.192476e-01   3.844346e-01   4.147291e-01   2.605392e-01 
## AccuracyPValue  McnemarPValue 
##   2.080789e-83            NaN
db_tda_pc_5.40.5_n1_db_nn1_cf0_ov_acc<-db_tda_pc_5.40.5_n1_db_nn1_cf0$overall[1]
db_tda_pc_5.40.5_n1_db_nn1_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.0000000   1.0000000            NaN      0.9029412        NA
## Class: BOMBAY     0.0000000   1.0000000            NaN      0.9617647        NA
## Class: CALI       0.0000000   1.0000000            NaN      0.8801471        NA
## Class: DERMASON   0.9802446   0.3526682      0.3479132      0.9806452 0.3479132
## Class: HOROZ      0.0000000   1.0000000            NaN      0.8583333        NA
## Class: SEKER      0.9671053   0.8568548      0.5419355      0.9933222 0.5419355
## Class: SIRA       0.0000000   1.0000000            NaN      0.8063725        NA
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.0000000        NA 0.09705882      0.0000000
## Class: BOMBAY   0.0000000        NA 0.03823529      0.0000000
## Class: CALI     0.0000000        NA 0.11985294      0.0000000
## Class: DERMASON 0.9802446 0.5135535 0.26053922      0.2553922
## Class: HOROZ    0.0000000        NA 0.14166667      0.0000000
## Class: SEKER    0.9671053 0.6946249 0.14901961      0.1441176
## Class: SIRA     0.0000000        NA 0.19362745      0.0000000
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA            0.0000000         0.5000000
## Class: BOMBAY              0.0000000         0.5000000
## Class: CALI                0.0000000         0.5000000
## Class: DERMASON            0.7340686         0.6664564
## Class: HOROZ               0.0000000         0.5000000
## Class: SEKER               0.2659314         0.9119801
## Class: SIRA                0.0000000         0.5000000
db_tda_pc_5.40.5_n1_db_nn1_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n1_db_nn1_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_pca_5.40.5_nn1_n1_3_fold<-(db_nn1_fit_re - db_tda_pc_5.40.5_n1_nn1_fit_re)
diff_drybean_tda_pca_5.40.5_nn1_n1_3_fold
##     Accuracy
## 1 -0.3817304
## 2 -0.1543897
## 3 -0.6382868
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_nn1.n1_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n1_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_nn1.n1_3_fold
## $probLeft
## [1] 0.75
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_nn1.n1_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_nn1.n1_3_fold$probLeft/bst_dbf_db_tda_pca_5.40.5_nn1.n1_3_fold$probRight
bst_dbf_db_tda_pca_5.40.5_nn1.n1_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_nn1.n1_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n1_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_nn1.n1_3_fold
## $winLeft
## [1] 0.9916333
## 
## $winRope
## [1] 0.008366667
## 
## $winRight
## [1] 0
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_nn1.n1_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n1_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_nn1.n1_3_fold
## $left
## [1] 0.9290595
## 
## $rope
## [1] 0.005602484
## 
## $right
## [1] 0.06533799
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.40.5_nn1_n1_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_nn1.n1_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n1_3_fold))
#bf_tda_pca_5.40.5_nn1.n1_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n1_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n1_3_fold)
## t = -2.8007, df = 2, p-value = 0.1073
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.9928675  0.2099296
## sample estimates:
##  mean of x 
## -0.3914689
### Test set diff
diff_drybean_tda_pca_5.40.5_nn1.n1_test<-(db_nn1_cf_ov_acc - db_tda_pc_5.40.5_n1_db_nn1_cf0_ov_acc)
diff_drybean_tda_pca_5.40.5_nn1.n1_test
##    Accuracy 
## -0.02156863
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_nn1.n1_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n1_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_nn1.n1_test
## $probLeft
## [1] 0.5
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_nn1.n1_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_nn1.n1_test$probLeft/bst_dbf_db_tda_pca_5.40.5_nn1.n1_test$probRight
bst_dbf_db_tda_pca_5.40.5_nn1.n1_test_odds.left
## [1] Inf
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_nn1.n1_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n1_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_nn1.n1_test
## $winLeft
## [1] 0.8429667
## 
## $winRope
## [1] 0.1570333
## 
## $winRight
## [1] 0
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_nn1.n1_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n1_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_nn1.n1_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_nn1.n1_test)))

#BayesFactor
#bf_tda_pca_5.40.5_nn1.n1_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n1_test)) #bf_tda_pca_5.40.5_nn1.n1_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n1_test))

##With TDA PCA filter 5 intervals, 50% overlap, 5 bins 
##Node2

##DryBean_TDA_PC_5.40.5_n2_NN1Fit0 <- nnet(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.40.5.n2.vec, size=2, range = 0.6,, type='class')

#Neural Network 1
DryBean_TDA_PC_5.40.5_n2_NN1Fit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.40.5.n2.vec, 
                           Importance = T,
                     method = 'nnet', 
                     trControl = fitControl,
                     tuneGrid = nn1Grid,
                     metric='Accuracy')
## # weights:  52
## initial  value 9510.224050 
## iter  10 value 8551.932276
## iter  20 value 8551.911335
## final  value 8551.911123 
## converged
## # weights:  75
## initial  value 12897.808669 
## iter  10 value 8551.711860
## final  value 8551.710010 
## converged
## # weights:  121
## initial  value 10304.114984 
## iter  10 value 8588.550128
## iter  20 value 8552.233914
## iter  30 value 8552.215566
## iter  40 value 8551.654436
## final  value 8551.642971 
## converged
## # weights:  167
## initial  value 10401.988943 
## iter  10 value 8553.000535
## iter  20 value 8551.199611
## iter  30 value 6902.647419
## iter  40 value 6589.095553
## iter  50 value 6507.675183
## iter  60 value 6232.462542
## iter  70 value 6081.590109
## iter  80 value 5964.429319
## iter  90 value 5626.325948
## iter 100 value 4672.470053
## final  value 4672.470053 
## stopped after 100 iterations
## # weights:  52
## initial  value 10370.035658 
## iter  10 value 8564.095952
## iter  20 value 8552.202429
## iter  30 value 8552.159200
## iter  40 value 8552.139652
## iter  40 value 8552.139628
## iter  50 value 8551.955803
## iter  50 value 8551.955795
## iter  50 value 8551.955795
## final  value 8551.955795 
## converged
## # weights:  75
## initial  value 9950.491512 
## iter  10 value 8558.954845
## iter  20 value 8552.270665
## iter  30 value 8551.731234
## iter  40 value 8528.520557
## iter  50 value 7393.219541
## iter  60 value 6834.512948
## iter  70 value 6135.811418
## iter  80 value 5589.058472
## iter  90 value 4410.530961
## iter 100 value 3816.362593
## final  value 3816.362593 
## stopped after 100 iterations
## # weights:  121
## initial  value 9630.938323 
## iter  10 value 8553.126549
## iter  20 value 8552.168929
## iter  30 value 8552.127758
## iter  40 value 8552.118275
## iter  50 value 8551.740526
## final  value 8551.738267 
## converged
## # weights:  167
## initial  value 9617.307486 
## iter  10 value 8642.867463
## iter  20 value 8554.350826
## iter  30 value 8552.200508
## iter  40 value 8552.174648
## iter  50 value 8552.027160
## iter  60 value 8551.964571
## iter  70 value 8551.949616
## iter  80 value 7090.194346
## iter  90 value 6883.054221
## iter 100 value 6692.450898
## final  value 6692.450898 
## stopped after 100 iterations
## # weights:  52
## initial  value 8884.037104 
## iter  10 value 8552.531758
## iter  20 value 8552.444678
## iter  30 value 8332.303637
## iter  40 value 7979.866928
## iter  50 value 7519.602547
## iter  60 value 6447.167892
## iter  70 value 6117.156196
## iter  80 value 5342.903916
## iter  90 value 5162.284280
## iter 100 value 4973.997267
## final  value 4973.997267 
## stopped after 100 iterations
## # weights:  75
## initial  value 8615.795225 
## iter  10 value 8552.459468
## iter  20 value 8552.444287
## iter  30 value 8552.183663
## iter  40 value 6994.229279
## iter  50 value 6774.014385
## iter  60 value 6535.861109
## iter  70 value 6514.450313
## iter  80 value 6509.221263
## iter  90 value 6507.898857
## iter 100 value 6366.709728
## final  value 6366.709728 
## stopped after 100 iterations
## # weights:  121
## initial  value 11188.652392 
## iter  10 value 8552.215043
## iter  20 value 8539.016003
## iter  30 value 7869.109678
## iter  40 value 6720.008132
## iter  50 value 6604.407433
## iter  60 value 6334.588313
## iter  70 value 5721.509527
## iter  80 value 4797.960617
## iter  90 value 4439.839843
## iter 100 value 4325.631844
## final  value 4325.631844 
## stopped after 100 iterations
## # weights:  167
## initial  value 10606.558287 
## iter  10 value 8877.174356
## iter  20 value 8546.197937
## iter  30 value 8509.159393
## iter  40 value 8185.512341
## iter  50 value 7392.273813
## iter  60 value 6821.810870
## iter  70 value 6677.645893
## iter  80 value 6294.510796
## iter  90 value 5957.808732
## iter 100 value 5351.184731
## final  value 5351.184731 
## stopped after 100 iterations
## # weights:  52
## initial  value 8920.338314 
## iter  10 value 8555.386444
## iter  20 value 8555.130235
## final  value 8554.991126 
## converged
## # weights:  75
## initial  value 9082.366453 
## iter  10 value 8554.928035
## final  value 8554.923959 
## converged
## # weights:  121
## initial  value 9888.932703 
## iter  10 value 8555.136934
## final  value 8555.127987 
## converged
## # weights:  167
## initial  value 11058.317406 
## iter  10 value 8554.931590
## final  value 8554.924153 
## converged
## # weights:  52
## initial  value 10073.202507 
## iter  10 value 8579.099813
## iter  20 value 8555.689980
## iter  30 value 8555.396682
## iter  40 value 8404.656378
## iter  50 value 6730.134085
## iter  60 value 6574.084871
## iter  70 value 6402.219090
## iter  80 value 5997.360478
## iter  90 value 5483.764962
## iter 100 value 5243.921408
## final  value 5243.921408 
## stopped after 100 iterations
## # weights:  75
## initial  value 9250.777327 
## iter  10 value 8559.217686
## iter  20 value 8555.443773
## iter  30 value 8555.356628
## iter  40 value 6852.894473
## iter  50 value 6806.224001
## iter  60 value 6611.400799
## iter  70 value 6490.197667
## iter  80 value 5790.822679
## iter  90 value 5156.261284
## iter 100 value 4488.026749
## final  value 4488.026749 
## stopped after 100 iterations
## # weights:  121
## initial  value 11251.399511 
## iter  10 value 8560.571658
## iter  20 value 8555.238604
## iter  30 value 8555.171982
## iter  30 value 8555.171935
## iter  40 value 8465.547114
## iter  50 value 8157.377044
## iter  60 value 7927.431195
## iter  70 value 7716.068680
## iter  80 value 7365.479377
## iter  90 value 6637.550892
## iter 100 value 6101.693775
## final  value 6101.693775 
## stopped after 100 iterations
## # weights:  167
## initial  value 9613.827103 
## iter  10 value 8555.783688
## iter  20 value 8555.177143
## iter  30 value 8555.015052
## iter  40 value 7567.405517
## iter  50 value 7196.515029
## iter  60 value 7039.321772
## iter  70 value 6716.767033
## iter  80 value 6573.395470
## iter  90 value 6404.232587
## iter 100 value 6275.596333
## final  value 6275.596333 
## stopped after 100 iterations
## # weights:  52
## initial  value 12530.764002 
## iter  10 value 8555.776871
## final  value 8555.661186 
## converged
## # weights:  75
## initial  value 10654.651317 
## iter  10 value 8555.738650
## iter  20 value 8554.564250
## iter  30 value 8453.362052
## iter  40 value 6894.475897
## iter  50 value 6824.710838
## iter  60 value 6286.883912
## iter  70 value 5669.380852
## iter  80 value 5551.380293
## iter  90 value 5537.413279
## iter 100 value 5182.899197
## final  value 5182.899197 
## stopped after 100 iterations
## # weights:  121
## initial  value 12906.822960 
## iter  10 value 8556.687980
## final  value 8555.192159 
## converged
## # weights:  167
## initial  value 9380.531494 
## iter  10 value 8357.809368
## iter  20 value 7223.392448
## iter  30 value 6470.525232
## iter  40 value 6244.518809
## iter  50 value 6144.357614
## iter  60 value 6052.569350
## iter  70 value 5978.510284
## iter  80 value 5895.591544
## iter  90 value 5854.295798
## iter 100 value 5773.489521
## final  value 5773.489521 
## stopped after 100 iterations
## # weights:  52
## initial  value 9779.016473 
## iter  10 value 8555.125414
## final  value 8555.125175 
## converged
## # weights:  75
## initial  value 10356.258885 
## iter  10 value 8559.375937
## iter  20 value 8555.182605
## iter  30 value 8555.054494
## iter  40 value 7138.503194
## iter  50 value 7005.614156
## iter  60 value 6415.668823
## iter  70 value 5897.983104
## iter  80 value 5290.288800
## iter  90 value 5067.624385
## iter 100 value 4882.627919
## final  value 4882.627919 
## stopped after 100 iterations
## # weights:  121
## initial  value 10759.765599 
## iter  10 value 8554.924743
## final  value 8554.924161 
## converged
## # weights:  167
## initial  value 13314.080400 
## iter  10 value 8679.493919
## iter  20 value 8555.179411
## iter  30 value 8555.008589
## iter  40 value 8554.884572
## final  value 8554.883598 
## converged
## # weights:  52
## initial  value 9649.388525 
## iter  10 value 8581.293677
## iter  20 value 8556.191017
## iter  30 value 8555.804201
## iter  40 value 8555.732310
## iter  50 value 8144.668793
## iter  60 value 7491.173230
## iter  70 value 7202.784729
## iter  80 value 6070.403230
## iter  90 value 5664.597954
## iter 100 value 5048.108903
## final  value 5048.108903 
## stopped after 100 iterations
## # weights:  75
## initial  value 9324.587160 
## iter  10 value 8559.513898
## iter  20 value 8555.106623
## iter  30 value 8555.058700
## final  value 8555.058077 
## converged
## # weights:  121
## initial  value 10556.762092 
## iter  10 value 8569.409393
## iter  20 value 8555.035982
## final  value 8554.994468 
## converged
## # weights:  167
## initial  value 12840.405637 
## iter  10 value 8571.580868
## iter  20 value 8555.148125
## iter  30 value 8554.992137
## final  value 8554.991429 
## converged
## # weights:  52
## initial  value 9344.674675 
## iter  10 value 8562.445928
## iter  20 value 8556.327418
## iter  30 value 7175.774693
## iter  40 value 6952.845635
## iter  50 value 6707.306684
## iter  60 value 6529.058284
## iter  70 value 6303.663139
## iter  80 value 4943.056681
## iter  90 value 3752.148866
## iter 100 value 3481.648229
## final  value 3481.648229 
## stopped after 100 iterations
## # weights:  75
## initial  value 9339.762172 
## iter  10 value 8555.367346
## iter  20 value 8555.346406
## iter  20 value 8555.346401
## iter  30 value 7803.740705
## iter  40 value 6891.193620
## iter  50 value 6747.730341
## iter  60 value 6551.091725
## iter  70 value 6417.308365
## iter  80 value 6164.951077
## iter  90 value 6066.749675
## iter 100 value 5817.884667
## final  value 5817.884667 
## stopped after 100 iterations
## # weights:  121
## initial  value 9588.226406 
## iter  10 value 8555.661125
## iter  20 value 8555.352123
## final  value 8555.348867 
## converged
## # weights:  167
## initial  value 9413.684780 
## iter  10 value 8555.063827
## iter  20 value 7663.898303
## iter  30 value 7234.722826
## iter  40 value 7096.309521
## iter  50 value 6809.715037
## iter  60 value 6692.208853
## iter  70 value 5810.491526
## iter  80 value 5463.737261
## iter  90 value 5334.245217
## iter 100 value 5215.628783
## final  value 5215.628783 
## stopped after 100 iterations
## # weights:  167
## initial  value 14113.844856 
## iter  10 value 12832.366992
## iter  20 value 12267.214770
## iter  30 value 11750.226298
## iter  40 value 10770.548785
## iter  50 value 10055.033387
## iter  60 value 9262.360212
## iter  70 value 8044.364602
## iter  80 value 7709.588795
## iter  90 value 7615.438743
## iter 100 value 7356.710004
## final  value 7356.710004 
## stopped after 100 iterations
DryBean_TDA_PC_5.40.5_n2_NN1Fit0
## Neural Network 
## 
## 8024 samples
##   16 predictor
##    6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 5348, 5350, 5350 
## Resampling results across tuning parameters:
## 
##   size  decay  Accuracy   Kappa    
##   2     0.3    0.3278914  0.0000000
##   2     0.5    0.5051539  0.3044746
##   2     0.7    0.5630479  0.3790604
##   3     0.3    0.4296116  0.1671979
##   3     0.5    0.5792312  0.4120493
##   3     0.7    0.5657092  0.3899554
##   5     0.3    0.3278914  0.0000000
##   5     0.5    0.3851090  0.1192024
##   5     0.7    0.4431131  0.1954446
##   7     0.3    0.4490922  0.1971020
##   7     0.5    0.4373213  0.2108351
##   7     0.7    0.6379645  0.5075824
## 
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 7 and decay = 0.7.
DryBean_TDA_PC_5.40.5_n2_NN1Fit0$resample
##    Accuracy     Kappa Resample
## 1 0.6342558 0.5155438    Fold2
## 2 0.6244395 0.4808225    Fold1
## 3 0.6551982 0.5263808    Fold3
db_tda_pc_5.40.5_n2_nn1_fit_re<-DryBean_TDA_PC_5.40.5_n2_NN1Fit0$resample[1]

summary(DryBean_TDA_PC_5.40.5_n2_NN1Fit0)
## a 16-7-6 network with 167 weights
## options were - softmax modelling  decay=0.7
##   b->h1  i1->h1  i2->h1  i3->h1  i4->h1  i5->h1  i6->h1  i7->h1  i8->h1  i9->h1 
##    0.00    0.10    1.10    0.98   -2.07    0.02    0.01   -0.11   -0.91    0.03 
## i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1 i16->h1 
##    0.00   -0.01    0.00    0.00    0.00   -0.01    0.00 
##   b->h2  i1->h2  i2->h2  i3->h2  i4->h2  i5->h2  i6->h2  i7->h2  i8->h2  i9->h2 
##   -0.05   -1.68   -2.72   -3.88   -2.05   -0.06   -0.09    1.75   -3.42    0.25 
## i10->h2 i11->h2 i12->h2 i13->h2 i14->h2 i15->h2 i16->h2 
##   -0.05   -0.04   -0.03    0.00    0.00   -0.02   -0.05 
##   b->h3  i1->h3  i2->h3  i3->h3  i4->h3  i5->h3  i6->h3  i7->h3  i8->h3  i9->h3 
##    0.00    0.06    0.00    0.00    0.00    0.00    0.00    0.07    0.00    0.00 
## i10->h3 i11->h3 i12->h3 i13->h3 i14->h3 i15->h3 i16->h3 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h4  i1->h4  i2->h4  i3->h4  i4->h4  i5->h4  i6->h4  i7->h4  i8->h4  i9->h4 
##    0.00    0.29    0.05    0.02    0.02    0.00    0.00    0.27    0.02    0.00 
## i10->h4 i11->h4 i12->h4 i13->h4 i14->h4 i15->h4 i16->h4 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h5  i1->h5  i2->h5  i3->h5  i4->h5  i5->h5  i6->h5  i7->h5  i8->h5  i9->h5 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
## i10->h5 i11->h5 i12->h5 i13->h5 i14->h5 i15->h5 i16->h5 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h6  i1->h6  i2->h6  i3->h6  i4->h6  i5->h6  i6->h6  i7->h6  i8->h6  i9->h6 
##   -0.09   -0.11    0.08    0.47    1.15    0.07   -0.20    0.15   -8.42    0.47 
## i10->h6 i11->h6 i12->h6 i13->h6 i14->h6 i15->h6 i16->h6 
##   -0.09   -0.13   -0.07    0.00    0.00   -0.01   -0.12 
##   b->h7  i1->h7  i2->h7  i3->h7  i4->h7  i5->h7  i6->h7  i7->h7  i8->h7  i9->h7 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
## i10->h7 i11->h7 i12->h7 i13->h7 i14->h7 i15->h7 i16->h7 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##  b->o1 h1->o1 h2->o1 h3->o1 h4->o1 h5->o1 h6->o1 h7->o1 
##  -2.38  -0.92   5.17   0.02  -0.02  -2.39   4.11   0.01 
##  b->o2 h1->o2 h2->o2 h3->o2 h4->o2 h5->o2 h6->o2 h7->o2 
##  -0.52  -1.74   3.54   0.12  -0.04  -0.51   1.29  -0.03 
##  b->o3 h1->o3 h2->o3 h3->o3 h4->o3 h5->o3 h6->o3 h7->o3 
##   1.79   1.52  -5.98  -0.22   0.16   1.81  -5.49  -0.01 
##  b->o4 h1->o4 h2->o4 h3->o4 h4->o4 h5->o4 h6->o4 h7->o4 
##  -0.56   1.80   0.30   0.48   0.00  -0.56   0.18   0.03 
##  b->o5 h1->o5 h2->o5 h3->o5 h4->o5 h5->o5 h6->o5 h7->o5 
##   0.55  -1.54  -3.31  -0.24  -0.16   0.56   1.15  -0.21 
##  b->o6 h1->o6 h2->o6 h3->o6 h4->o6 h5->o6 h6->o6 h7->o6 
##   1.11   0.90   0.28  -0.16   0.06   1.09  -1.25   0.22
#vip(DryBean_TDA_PC_5.40.5_n2_NN1Fit0,25) + ggtitle("dryBean_TDA_PCA_5.40.5_n2_NN1Fit TDA-Assited NN")


# Predict outcome using DryBean_TDA_PC_5.40.5_n2_NN1Fit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.40.5_n2_NN1Fit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.40.5_n2_db_nn1_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length

## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
db_tda_pc_5.40.5_n2_db_nn1_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      382    156  469        2   178     9   29
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    1        0     0     0    0
##   DERMASON        0      0    0     1012    18   343  101
##   HOROZ           0      0    0        0     0     0    0
##   SEKER           6      0    3        5     0   228   52
##   SIRA            8      0   16       44   382    28  608
## 
## Overall Statistics
##                                           
##                Accuracy : 0.5468          
##                  95% CI : (0.5314, 0.5622)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.4436          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.96465       0.00000   0.0020450          0.9520
## Specificity                  0.77117       1.00000   1.0000000          0.8469
## Pos Pred Value               0.31184           NaN   1.0000000          0.6866
## Neg Pred Value               0.99510       0.96176   0.8803628          0.9804
## Prevalence                   0.09706       0.03824   0.1198529          0.2605
## Detection Rate               0.09363       0.00000   0.0002451          0.2480
## Detection Prevalence         0.30025       0.00000   0.0002451          0.3613
## Balanced Accuracy            0.86791       0.50000   0.5010225          0.8994
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.0000      0.37500      0.7696
## Specificity                1.0000      0.98099      0.8547
## Pos Pred Value                NaN      0.77551      0.5599
## Neg Pred Value             0.8583      0.89963      0.9392
## Prevalence                 0.1417      0.14902      0.1936
## Detection Rate             0.0000      0.05588      0.1490
## Detection Prevalence       0.0000      0.07206      0.2662
## Balanced Accuracy          0.5000      0.67800      0.8122
db_tda_pc_5.40.5_n2_db_nn1_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      382    156  469        2   178     9   29
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    1        0     0     0    0
##   DERMASON        0      0    0     1012    18   343  101
##   HOROZ           0      0    0        0     0     0    0
##   SEKER           6      0    3        5     0   228   52
##   SIRA            8      0   16       44   382    28  608
## 
## Overall Statistics
##                                           
##                Accuracy : 0.5468          
##                  95% CI : (0.5314, 0.5622)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.4436          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.96465       0.00000   0.0020450          0.9520
## Specificity                  0.77117       1.00000   1.0000000          0.8469
## Pos Pred Value               0.31184           NaN   1.0000000          0.6866
## Neg Pred Value               0.99510       0.96176   0.8803628          0.9804
## Prevalence                   0.09706       0.03824   0.1198529          0.2605
## Detection Rate               0.09363       0.00000   0.0002451          0.2480
## Detection Prevalence         0.30025       0.00000   0.0002451          0.3613
## Balanced Accuracy            0.86791       0.50000   0.5010225          0.8994
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.0000      0.37500      0.7696
## Specificity                1.0000      0.98099      0.8547
## Pos Pred Value                NaN      0.77551      0.5599
## Neg Pred Value             0.8583      0.89963      0.9392
## Prevalence                 0.1417      0.14902      0.1936
## Detection Rate             0.0000      0.05588      0.1490
## Detection Prevalence       0.0000      0.07206      0.2662
## Balanced Accuracy          0.5000      0.67800      0.8122
db_tda_pc_5.40.5_n2_db_nn1_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.5468137      0.4435512      0.5313874      0.5621729      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
db_tda_pc_5.40.5_n2_db_nn1_cf0_ov_acc<-db_tda_pc_5.40.5_n2_db_nn1_cf0$overall[1]
db_tda_pc_5.40.5_n2_db_nn1_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA  0.96464646   0.7711726      0.3118367      0.9950963 0.3118367
## Class: BOMBAY    0.00000000   1.0000000            NaN      0.9617647        NA
## Class: CALI      0.00204499   1.0000000      1.0000000      0.8803628 1.0000000
## Class: DERMASON  0.95202258   0.8468677      0.6865672      0.9804298 0.6865672
## Class: HOROZ     0.00000000   1.0000000            NaN      0.8583333        NA
## Class: SEKER     0.37500000   0.9809908      0.7755102      0.8996302 0.7755102
## Class: SIRA      0.76962025   0.8547112      0.5598527      0.9392118 0.5598527
##                     Recall          F1 Prevalence Detection Rate
## Class: BARBUNYA 0.96464646 0.471314004 0.09705882    0.093627451
## Class: BOMBAY   0.00000000          NA 0.03823529    0.000000000
## Class: CALI     0.00204499 0.004081633 0.11985294    0.000245098
## Class: DERMASON 0.95202258 0.797792669 0.26053922    0.248039216
## Class: HOROZ    0.00000000          NA 0.14166667    0.000000000
## Class: SEKER    0.37500000 0.505543237 0.14901961    0.055882353
## Class: SIRA     0.76962025 0.648187633 0.19362745    0.149019608
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA          0.300245098         0.8679096
## Class: BOMBAY            0.000000000         0.5000000
## Class: CALI              0.000245098         0.5010225
## Class: DERMASON          0.361274510         0.8994452
## Class: HOROZ             0.000000000         0.5000000
## Class: SEKER             0.072058824         0.6779954
## Class: SIRA              0.266176471         0.8121657
db_tda_pc_5.40.5_n2_db_nn1_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n2_db_nn1_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_pca_5.40.5_nn1_n2_3_fold<-(db_nn1_fit_re - db_tda_pc_5.40.5_n2_nn1_fit_re)
diff_drybean_tda_pca_5.40.5_nn1_n2_3_fold
##     Accuracy
## 1 -0.1599089
## 2  0.1149373
## 3 -0.3948897
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_nn1.n2_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n2_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_nn1.n2_3_fold
## $probLeft
## [1] 0.5
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0.25
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_nn1.n2_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_nn1.n2_3_fold$probLeft/bst_dbf_db_tda_pca_5.40.5_nn1.n2_3_fold$probRight
bst_dbf_db_tda_pca_5.40.5_nn1.n2_3_fold_odds.left
## [1] 2
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_nn1.n2_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n2_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_nn1.n2_3_fold
## $winLeft
## [1] 0.8771667
## 
## $winRope
## [1] 0.0152
## 
## $winRight
## [1] 0.1076333
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_nn1.n2_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n2_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_nn1.n2_3_fold
## $left
## [1] 0.7469057
## 
## $rope
## [1] 0.02588687
## 
## $right
## [1] 0.2272075
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.40.5_nn1_n2_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_nn1.n2_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n2_3_fold))
#bf_tda_pca_5.40.5_nn1.n2_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n2_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n2_3_fold)
## t = -0.99522, df = 2, p-value = 0.4245
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.7805057  0.4872648
## sample estimates:
##  mean of x 
## -0.1466205
### Test set diff
diff_drybean_tda_pca_5.40.5_nn1.n2_test<-(db_nn1_cf_ov_acc - db_tda_pc_5.40.5_n2_db_nn1_cf0_ov_acc)
diff_drybean_tda_pca_5.40.5_nn1.n2_test
##   Accuracy 
## -0.1688725
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_nn1.n2_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n2_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_nn1.n2_test
## $probLeft
## [1] 0.5
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_nn1.n2_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_nn1.n2_test$probLeft/bst_dbf_db_tda_pca_5.40.5_nn1.n2_test$probRight
bst_dbf_db_tda_pca_5.40.5_nn1.n2_test_odds.left
## [1] Inf
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_nn1.n2_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n2_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_nn1.n2_test
## $winLeft
## [1] 0.8369667
## 
## $winRope
## [1] 0.1630333
## 
## $winRight
## [1] 0
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_nn1.n2_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n2_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_nn1.n2_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_nn1.n2_test)))

#BayesFactor
#bf_tda_pca_5.40.5_nn1.n2_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n2_test)) #bf_tda_pca_5.40.5_nn1.n2_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n2_test))


##With TDA PCA filter 5 intervals, 50% overlap, 5 bins 
##Node3

#Neural Network 1
DryBean_TDA_PC_5.40.5_n3_NN1Fit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.40.5.n3.vec, 
                           Importance = T,
                     method = 'nnet', 
                     trControl = fitControl,
                     tuneGrid = nn1Grid,
                     metric='Accuracy')
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'DERMASON' is empty
## # weights:  52
## initial  value 7862.207380 
## iter  10 value 4354.956656
## iter  20 value 4215.743261
## iter  30 value 4214.812584
## iter  40 value 3664.957510
## iter  50 value 3267.787622
## iter  60 value 2518.195530
## iter  70 value 2371.900467
## iter  80 value 2127.590550
## iter  90 value 1612.027331
## iter 100 value 1411.419480
## final  value 1411.419480 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'DERMASON' is empty
## # weights:  75
## initial  value 8116.512427 
## iter  10 value 4245.566197
## iter  20 value 4245.296254
## iter  30 value 4243.038650
## iter  40 value 3644.261259
## iter  50 value 3202.003922
## iter  60 value 2833.315184
## iter  70 value 2498.643586
## iter  80 value 2415.644926
## iter  90 value 2380.590779
## iter 100 value 2326.963630
## final  value 2326.963630 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'DERMASON' is empty
## # weights:  121
## initial  value 7508.979846 
## iter  10 value 4290.124208
## iter  20 value 4238.824824
## iter  30 value 4234.138082
## iter  40 value 4230.641752
## iter  50 value 4226.012185
## iter  60 value 4205.322502
## iter  70 value 2598.561240
## iter  80 value 2455.536675
## iter  90 value 2369.656256
## iter 100 value 2340.592071
## final  value 2340.592071 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'DERMASON' is empty
## # weights:  167
## initial  value 6360.126308 
## iter  10 value 4241.667201
## iter  20 value 4237.439497
## iter  30 value 4232.333920
## iter  40 value 3852.765716
## iter  50 value 3775.341519
## iter  60 value 3713.470766
## iter  70 value 3093.197681
## iter  80 value 2879.369625
## iter  90 value 2587.840848
## iter 100 value 2491.115278
## final  value 2491.115278 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'DERMASON' is empty
## # weights:  52
## initial  value 5874.053784 
## iter  10 value 4248.787788
## iter  20 value 4241.000708
## iter  30 value 4234.272036
## iter  40 value 4234.134010
## iter  50 value 4233.973171
## final  value 4233.969767 
## converged
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'DERMASON' is empty
## # weights:  75
## initial  value 6655.826698 
## iter  10 value 4355.234017
## iter  20 value 4217.507813
## iter  30 value 3365.752181
## iter  40 value 3257.456215
## iter  50 value 2983.990042
## iter  60 value 2589.512176
## iter  70 value 2404.462386
## iter  80 value 2240.721656
## iter  90 value 1994.557083
## iter 100 value 1742.592612
## final  value 1742.592612 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'DERMASON' is empty
## # weights:  121
## initial  value 7257.887238 
## iter  10 value 4267.923604
## iter  20 value 4258.319925
## iter  30 value 3979.475692
## iter  40 value 3533.846341
## iter  50 value 3382.434944
## iter  60 value 2926.929542
## iter  70 value 2734.353867
## iter  80 value 2454.828441
## iter  90 value 2350.436319
## iter 100 value 2154.843862
## final  value 2154.843862 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'DERMASON' is empty
## # weights:  167
## initial  value 6403.811085 
## iter  10 value 4279.343103
## iter  20 value 4236.243765
## iter  30 value 4234.831105
## iter  40 value 4228.381119
## iter  50 value 4164.398012
## iter  60 value 3329.027094
## iter  70 value 3220.261381
## iter  80 value 2840.034512
## iter  90 value 2491.874738
## iter 100 value 2399.436862
## final  value 2399.436862 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'DERMASON' is empty
## # weights:  52
## initial  value 5770.444073 
## iter  10 value 4241.502198
## iter  20 value 4241.495733
## iter  30 value 4237.533360
## iter  40 value 4096.062190
## iter  50 value 4021.839159
## iter  60 value 2909.070220
## iter  70 value 2411.836387
## iter  80 value 1598.887350
## iter  90 value 1459.086375
## iter 100 value 1339.059093
## final  value 1339.059093 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'DERMASON' is empty
## # weights:  75
## initial  value 5376.852633 
## iter  10 value 4244.057942
## iter  20 value 4241.638811
## iter  30 value 4238.548647
## iter  40 value 4238.309407
## iter  40 value 4238.309407
## iter  40 value 4238.309389
## final  value 4238.309389 
## converged
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'DERMASON' is empty
## # weights:  121
## initial  value 6234.514364 
## iter  10 value 4257.213815
## iter  20 value 4245.829023
## iter  30 value 4236.625551
## iter  40 value 4236.619418
## iter  40 value 4236.619415
## iter  50 value 4236.193655
## iter  60 value 4235.105533
## iter  70 value 4163.001752
## iter  80 value 3901.228138
## iter  90 value 3843.850900
## iter 100 value 3769.110087
## final  value 3769.110087 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'DERMASON' is empty
## # weights:  167
## initial  value 8691.704546 
## iter  10 value 4257.580870
## iter  20 value 4240.902534
## iter  30 value 2919.317152
## iter  40 value 2607.304046
## iter  50 value 2371.593587
## iter  60 value 2213.420305
## iter  70 value 2147.862180
## iter  80 value 1996.041360
## iter  90 value 1914.020822
## iter 100 value 1853.127407
## final  value 1853.127407 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'SEKER' is empty
## # weights:  52
## initial  value 5365.362172 
## iter  10 value 4235.291801
## iter  20 value 4233.002688
## iter  30 value 4232.959035
## iter  40 value 4030.302513
## iter  50 value 3935.838807
## iter  60 value 3313.096848
## iter  70 value 2413.462614
## iter  80 value 2228.429500
## iter  90 value 2082.397758
## iter 100 value 1953.276556
## final  value 1953.276556 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'SEKER' is empty
## # weights:  75
## initial  value 6087.713873 
## iter  10 value 4255.720703
## iter  20 value 4234.584964
## iter  30 value 4233.236197
## iter  40 value 3937.347068
## iter  50 value 3770.083556
## iter  60 value 3192.790521
## iter  70 value 2487.796522
## iter  80 value 2284.949769
## iter  90 value 2220.142429
## iter 100 value 1743.269444
## final  value 1743.269444 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'SEKER' is empty
## # weights:  121
## initial  value 10252.439071 
## iter  10 value 4245.156231
## iter  20 value 4202.740282
## iter  30 value 4051.064773
## iter  40 value 4025.743775
## iter  50 value 3888.665839
## iter  60 value 3168.022327
## iter  70 value 3012.155667
## iter  80 value 2690.560008
## iter  90 value 2219.122920
## iter 100 value 1974.596750
## final  value 1974.596750 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'SEKER' is empty
## # weights:  167
## initial  value 5875.857607 
## iter  10 value 4260.066339
## iter  20 value 4252.103008
## iter  30 value 4217.154079
## iter  40 value 3189.719439
## iter  50 value 2709.387739
## iter  60 value 2162.203198
## iter  70 value 1747.939617
## iter  80 value 1612.939610
## iter  90 value 1561.819994
## iter 100 value 1439.624976
## final  value 1439.624976 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'SEKER' is empty
## # weights:  52
## initial  value 7776.421021 
## iter  10 value 4259.702625
## iter  20 value 4246.889274
## iter  30 value 4240.452428
## iter  40 value 3414.009058
## iter  50 value 2823.032672
## iter  60 value 2281.202485
## iter  70 value 2218.968825
## iter  80 value 2146.775026
## iter  90 value 1928.747479
## iter 100 value 1832.842219
## final  value 1832.842219 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'SEKER' is empty
## # weights:  75
## initial  value 8664.862161 
## iter  10 value 4277.742685
## iter  20 value 4233.531679
## iter  30 value 4233.219379
## iter  40 value 4232.558381
## final  value 4232.555570 
## converged
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'SEKER' is empty
## # weights:  121
## initial  value 7974.384335 
## iter  10 value 4373.021065
## iter  20 value 4259.881622
## iter  30 value 4258.367128
## iter  40 value 4252.564636
## iter  50 value 4230.460069
## iter  60 value 4081.325667
## iter  70 value 3772.276657
## iter  80 value 3220.503714
## iter  90 value 2571.862203
## iter 100 value 2528.183612
## final  value 2528.183612 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'SEKER' is empty
## # weights:  167
## initial  value 10745.657631 
## iter  10 value 4605.114115
## iter  20 value 4308.131178
## iter  30 value 4302.775233
## iter  40 value 4292.148416
## iter  50 value 4120.051660
## iter  60 value 3667.886678
## iter  70 value 2937.142869
## iter  80 value 2497.619995
## iter  90 value 2275.947602
## iter 100 value 2195.248395
## final  value 2195.248395 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'SEKER' is empty
## # weights:  52
## initial  value 6339.657547 
## iter  10 value 4241.808984
## iter  20 value 4240.225154
## iter  30 value 4235.049021
## iter  40 value 3139.675610
## iter  50 value 2831.182944
## iter  60 value 2476.736943
## iter  70 value 2298.750487
## iter  80 value 2237.382126
## iter  90 value 2184.442918
## iter 100 value 2082.868478
## final  value 2082.868478 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'SEKER' is empty
## # weights:  75
## initial  value 6730.541122 
## iter  10 value 4759.494573
## iter  20 value 4246.888969
## iter  30 value 4238.386917
## iter  40 value 4222.769439
## iter  50 value 3884.969620
## iter  60 value 2707.629103
## iter  70 value 2064.399476
## iter  80 value 1716.173802
## iter  90 value 1540.937233
## iter 100 value 1482.621605
## final  value 1482.621605 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'SEKER' is empty
## # weights:  121
## initial  value 5730.031646 
## iter  10 value 4246.441972
## iter  20 value 4234.780964
## iter  30 value 4233.049157
## iter  40 value 4232.696660
## iter  50 value 3671.811764
## iter  60 value 3557.890524
## iter  70 value 3509.855339
## iter  80 value 3132.826302
## iter  90 value 2725.371143
## iter 100 value 2571.491594
## final  value 2571.491594 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'SEKER' is empty
## # weights:  167
## initial  value 5945.945848 
## iter  10 value 4240.715069
## iter  20 value 4140.071328
## iter  30 value 3904.050380
## iter  40 value 3780.680634
## iter  50 value 3605.530761
## iter  60 value 3008.089666
## iter  70 value 2560.956424
## iter  80 value 2359.942896
## iter  90 value 2086.300816
## iter 100 value 1948.836200
## final  value 1948.836200 
## stopped after 100 iterations
## # weights:  55
## initial  value 6601.807023 
## iter  10 value 4245.703555
## iter  20 value 4245.210453
## iter  30 value 4216.462083
## iter  40 value 3665.218847
## iter  50 value 3496.790617
## iter  60 value 3481.590046
## iter  70 value 2979.136175
## iter  80 value 2630.984492
## iter  90 value 2501.548157
## iter 100 value 2372.482860
## final  value 2372.482860 
## stopped after 100 iterations
## # weights:  79
## initial  value 6932.287807 
## iter  10 value 4347.163498
## iter  20 value 4272.970535
## iter  30 value 4269.441714
## iter  40 value 3786.041215
## iter  50 value 3477.993794
## iter  60 value 2999.262775
## iter  70 value 2850.153239
## iter  80 value 2613.065258
## iter  90 value 2330.078006
## iter 100 value 2048.429627
## final  value 2048.429627 
## stopped after 100 iterations
## # weights:  127
## initial  value 6626.477301 
## iter  10 value 4285.174454
## iter  20 value 4277.934733
## iter  30 value 4274.717687
## iter  40 value 3233.826222
## iter  50 value 2614.562464
## iter  60 value 2429.843314
## iter  70 value 2388.199433
## iter  80 value 2349.003525
## iter  90 value 2325.129578
## iter 100 value 2319.658810
## final  value 2319.658810 
## stopped after 100 iterations
## # weights:  175
## initial  value 4772.049384 
## iter  10 value 4237.678659
## iter  20 value 4236.455229
## iter  30 value 4236.152807
## iter  40 value 4235.934361
## iter  50 value 4081.077298
## iter  60 value 3981.335514
## iter  70 value 3372.587851
## iter  80 value 2629.155033
## iter  90 value 2440.592264
## iter 100 value 2410.866862
## final  value 2410.866862 
## stopped after 100 iterations
## # weights:  55
## initial  value 6467.617490 
## iter  10 value 4273.710107
## iter  20 value 4251.526745
## iter  30 value 4222.283616
## iter  40 value 3588.913087
## iter  50 value 3449.088249
## iter  60 value 3265.591821
## iter  70 value 3010.091173
## iter  80 value 2552.672586
## iter  90 value 2302.815339
## iter 100 value 1740.730393
## final  value 1740.730393 
## stopped after 100 iterations
## # weights:  79
## initial  value 7667.102476 
## iter  10 value 4436.351588
## iter  20 value 4260.992080
## iter  30 value 4242.911974
## iter  40 value 4204.216577
## iter  50 value 3690.595254
## iter  60 value 3590.618816
## iter  70 value 3392.915376
## iter  80 value 3292.603295
## iter  90 value 3245.377923
## iter 100 value 2972.713590
## final  value 2972.713590 
## stopped after 100 iterations
## # weights:  127
## initial  value 6695.725108 
## iter  10 value 4261.146017
## iter  20 value 4251.807871
## iter  30 value 4251.587108
## iter  40 value 4246.018949
## iter  50 value 4244.597106
## iter  60 value 3849.503058
## iter  70 value 2883.810039
## iter  80 value 2824.505696
## iter  90 value 2741.100369
## iter 100 value 2627.648528
## final  value 2627.648528 
## stopped after 100 iterations
## # weights:  175
## initial  value 8216.017323 
## iter  10 value 4444.003521
## iter  20 value 4294.218242
## iter  30 value 4288.516238
## iter  40 value 3768.759299
## iter  50 value 2774.172367
## iter  60 value 2606.240655
## iter  70 value 2408.590742
## iter  80 value 2085.297155
## iter  90 value 1671.081176
## iter 100 value 1614.257104
## final  value 1614.257104 
## stopped after 100 iterations
## # weights:  55
## initial  value 6303.534786 
## iter  10 value 4251.921408
## iter  20 value 4249.208917
## iter  30 value 3887.508054
## iter  40 value 3678.199165
## iter  50 value 3017.383212
## iter  60 value 2887.428249
## iter  70 value 2662.604462
## iter  80 value 2566.420485
## iter  90 value 2556.893148
## iter 100 value 2478.994419
## final  value 2478.994419 
## stopped after 100 iterations
## # weights:  79
## initial  value 6717.876767 
## iter  10 value 4314.404734
## iter  20 value 4255.705943
## iter  30 value 4252.228363
## iter  40 value 4250.180836
## iter  50 value 4247.052634
## iter  60 value 4246.341941
## iter  70 value 4245.871966
## iter  80 value 4233.631865
## iter  90 value 3994.559393
## iter 100 value 3344.485623
## final  value 3344.485623 
## stopped after 100 iterations
## # weights:  127
## initial  value 5664.072115 
## iter  10 value 4263.709528
## iter  20 value 4254.331149
## iter  30 value 4254.136116
## iter  40 value 4216.563972
## iter  50 value 3520.814265
## iter  60 value 3232.311023
## iter  70 value 2858.647759
## iter  80 value 2618.187752
## iter  90 value 2087.649508
## iter 100 value 1576.320169
## final  value 1576.320169 
## stopped after 100 iterations
## # weights:  175
## initial  value 8407.283195 
## iter  10 value 4276.541490
## iter  20 value 4273.555792
## iter  30 value 4229.481289
## iter  40 value 4133.833692
## iter  50 value 3389.045282
## iter  60 value 3065.491835
## iter  70 value 2907.047723
## iter  80 value 2795.949305
## iter  90 value 2623.341038
## iter 100 value 2338.119377
## final  value 2338.119377 
## stopped after 100 iterations
## # weights:  55
## initial  value 8823.655070 
## iter  10 value 6378.051995
## iter  20 value 6371.367664
## iter  30 value 5184.679088
## iter  40 value 3876.332824
## iter  50 value 3587.226504
## iter  60 value 3556.793864
## iter  70 value 3462.931154
## iter  80 value 3357.108377
## iter  90 value 2767.396913
## iter 100 value 2085.451202
## final  value 2085.451202 
## stopped after 100 iterations
DryBean_TDA_PC_5.40.5_n3_NN1Fit0
## Neural Network 
## 
## 5008 samples
##   16 predictor
##    7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 3339, 3338, 3339 
## Resampling results across tuning parameters:
## 
##   size  decay  Accuracy   Kappa    
##   2     0.3    0.6768840  0.5629723
##   2     0.5    0.5724238  0.5303819
##   2     0.7    0.6826762  0.5694244
##   3     0.3    0.6738859  0.5742623
##   3     0.5    0.5102103  0.2904621
##   3     0.7    0.5083118  0.4229458
##   5     0.3    0.6117775  0.4766277
##   5     0.5    0.6076133  0.4575038
##   5     0.7    0.5670705  0.4726360
##   7     0.3    0.6369307  0.5235983
##   7     0.5    0.6613152  0.5554302
##   7     0.7    0.6529325  0.5399231
## 
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 2 and decay = 0.7.
DryBean_TDA_PC_5.40.5_n3_NN1Fit0$resample
##    Accuracy     Kappa Resample
## 1 0.6878370 0.5225931    Fold3
## 2 0.8401198 0.7619397    Fold2
## 3 0.5200719 0.4237405    Fold1
db_tda_pc_5.40.5_n3_nn1_fit_re<-DryBean_TDA_PC_5.40.5_n3_NN1Fit0$resample[1]

summary(DryBean_TDA_PC_5.40.5_n3_NN1Fit0)
## a 16-2-7 network with 55 weights
## options were - softmax modelling  decay=0.7
##   b->h1  i1->h1  i2->h1  i3->h1  i4->h1  i5->h1  i6->h1  i7->h1  i8->h1  i9->h1 
##    0.35    0.00   -0.01   -0.24   -0.22    0.32    0.38    0.00    0.42    1.90 
## i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1 i16->h1 
##    0.34    0.21    0.23    0.01    0.00    0.12    0.30 
##   b->h2  i1->h2  i2->h2  i3->h2  i4->h2  i5->h2  i6->h2  i7->h2  i8->h2  i9->h2 
##   -0.16    0.02    0.54   -1.09   -0.95    0.81   -0.63   -0.02    0.31   -0.28 
## i10->h2 i11->h2 i12->h2 i13->h2 i14->h2 i15->h2 i16->h2 
##   -0.16   -0.22    0.03    0.00    0.00    0.29    0.08 
##  b->o1 h1->o1 h2->o1 
##  -1.79   3.81   3.73 
##  b->o2 h1->o2 h2->o2 
##  -2.60   4.69  -0.84 
##  b->o3 h1->o3 h2->o3 
##   0.89   3.54  -1.57 
##  b->o4 h1->o4 h2->o4 
##  -1.64  -0.97  -0.55 
##  b->o5 h1->o5 h2->o5 
##   4.97  -8.11  -0.51 
##  b->o6 h1->o6 h2->o6 
##  -1.89  -1.06   0.00 
##  b->o7 h1->o7 h2->o7 
##   2.07  -1.89  -0.26
#vip(DryBean_TDA_PC_5.40.5_n3_NN1Fit0,25) + ggtitle("dryBean_TDA_PCA_5.40.5_n3_NN1Fit TDA-Assited NN")

# Predict outcome using DryBean_TDA_PC_5.40.5_n3_NN1Fit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.40.5_n3_NN1Fit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.40.5_n3_db_nn1_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
db_tda_pc_5.40.5_n3_db_nn1_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      327      7   18        5     4    78   20
##   BOMBAY          0      0    0        0     0     0    0
##   CALI           62    149  459       96     9   527  396
##   DERMASON        0      0    0        0     0     0    0
##   HOROZ           7      0   12      962   565     3  374
##   SEKER           0      0    0        0     0     0    0
##   SIRA            0      0    0        0     0     0    0
## 
## Overall Statistics
##                                           
##                Accuracy : 0.3311          
##                  95% CI : (0.3167, 0.3458)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.2333          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.82576       0.00000      0.9387          0.0000
## Specificity                  0.96417       1.00000      0.6550          1.0000
## Pos Pred Value               0.71242           NaN      0.2703             NaN
## Neg Pred Value               0.98094       0.96176      0.9874          0.7395
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.08015       0.00000      0.1125          0.0000
## Detection Prevalence         0.11250       0.00000      0.4162          0.0000
## Balanced Accuracy            0.89496       0.50000      0.7968          0.5000
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9775        0.000      0.0000
## Specificity                0.6122        1.000      1.0000
## Pos Pred Value             0.2938          NaN         NaN
## Neg Pred Value             0.9940        0.851      0.8064
## Prevalence                 0.1417        0.149      0.1936
## Detection Rate             0.1385        0.000      0.0000
## Detection Prevalence       0.4713        0.000      0.0000
## Balanced Accuracy          0.7949        0.500      0.5000
db_tda_pc_5.40.5_n3_db_nn1_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      327      7   18        5     4    78   20
##   BOMBAY          0      0    0        0     0     0    0
##   CALI           62    149  459       96     9   527  396
##   DERMASON        0      0    0        0     0     0    0
##   HOROZ           7      0   12      962   565     3  374
##   SEKER           0      0    0        0     0     0    0
##   SIRA            0      0    0        0     0     0    0
## 
## Overall Statistics
##                                           
##                Accuracy : 0.3311          
##                  95% CI : (0.3167, 0.3458)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.2333          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.82576       0.00000      0.9387          0.0000
## Specificity                  0.96417       1.00000      0.6550          1.0000
## Pos Pred Value               0.71242           NaN      0.2703             NaN
## Neg Pred Value               0.98094       0.96176      0.9874          0.7395
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.08015       0.00000      0.1125          0.0000
## Detection Prevalence         0.11250       0.00000      0.4162          0.0000
## Balanced Accuracy            0.89496       0.50000      0.7968          0.5000
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9775        0.000      0.0000
## Specificity                0.6122        1.000      1.0000
## Pos Pred Value             0.2938          NaN         NaN
## Neg Pred Value             0.9940        0.851      0.8064
## Prevalence                 0.1417        0.149      0.1936
## Detection Rate             0.1385        0.000      0.0000
## Detection Prevalence       0.4713        0.000      0.0000
## Balanced Accuracy          0.7949        0.500      0.5000
db_tda_pc_5.40.5_n3_db_nn1_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   3.311275e-01   2.333225e-01   3.166894e-01   3.458077e-01   2.605392e-01 
## AccuracyPValue  McnemarPValue 
##   7.904289e-24            NaN
db_tda_pc_5.40.5_n3_db_nn1_cf0_ov_acc<-db_tda_pc_5.40.5_n3_db_nn1_cf0$overall[1]
db_tda_pc_5.40.5_n3_db_nn1_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.8257576   0.9641694      0.7124183      0.9809445 0.7124183
## Class: BOMBAY     0.0000000   1.0000000            NaN      0.9617647        NA
## Class: CALI       0.9386503   0.6549708      0.2703180      0.9874055 0.2703180
## Class: DERMASON   0.0000000   1.0000000            NaN      0.7394608        NA
## Class: HOROZ      0.9775087   0.6122216      0.2938118      0.9939731 0.2938118
## Class: SEKER      0.0000000   1.0000000            NaN      0.8509804        NA
## Class: SIRA       0.0000000   1.0000000            NaN      0.8063725        NA
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.8257576 0.7649123 0.09705882     0.08014706
## Class: BOMBAY   0.0000000        NA 0.03823529     0.00000000
## Class: CALI     0.9386503 0.4197531 0.11985294     0.11250000
## Class: DERMASON 0.0000000        NA 0.26053922     0.00000000
## Class: HOROZ    0.9775087 0.4518193 0.14166667     0.13848039
## Class: SEKER    0.0000000        NA 0.14901961     0.00000000
## Class: SIRA     0.0000000        NA 0.19362745     0.00000000
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA            0.1125000         0.8949635
## Class: BOMBAY              0.0000000         0.5000000
## Class: CALI                0.4161765         0.7968105
## Class: DERMASON            0.0000000         0.5000000
## Class: HOROZ               0.4713235         0.7948651
## Class: SEKER               0.0000000         0.5000000
## Class: SIRA                0.0000000         0.5000000
db_tda_pc_5.40.5_n3_db_nn1_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n3_db_nn1_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_pca_5.40.5_nn1_n3_3_fold<-(db_nn1_fit_re - db_tda_pc_5.40.5_n3_nn1_fit_re)
diff_drybean_tda_pca_5.40.5_nn1_n3_3_fold
##     Accuracy
## 1 -0.2134902
## 2 -0.1007430
## 3 -0.2597634
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_nn1.n3_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n3_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_nn1.n3_3_fold
## $probLeft
## [1] 0.75
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_nn1.n3_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_nn1.n3_3_fold$probLeft/bst_dbf_db_tda_pca_5.40.5_nn1.n3_3_fold$probRight
bst_dbf_db_tda_pca_5.40.5_nn1.n3_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_nn1.n3_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n3_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_nn1.n3_3_fold
## $winLeft
## [1] 0.9912667
## 
## $winRope
## [1] 0.008733333
## 
## $winRight
## [1] 0
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_nn1.n3_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n3_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_nn1.n3_3_fold
## $left
## [1] 0.9601206
## 
## $rope
## [1] 0.006800542
## 
## $right
## [1] 0.03307883
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.40.5_nn1_n3_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_nn1.n3_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n3_3_fold))
#bf_tda_pca_5.40.5_nn1.n3_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n3_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n3_3_fold)
## t = -4.0517, df = 2, p-value = 0.05586
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.39451745  0.01185306
## sample estimates:
##  mean of x 
## -0.1913322
### Test set diff
diff_drybean_tda_pca_5.40.5_nn1.n3_test<-(db_nn1_cf_ov_acc - db_tda_pc_5.40.5_n3_db_nn1_cf0_ov_acc)
diff_drybean_tda_pca_5.40.5_nn1.n3_test
##   Accuracy 
## 0.04681373
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_nn1.n3_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n3_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_nn1.n3_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_nn1.n3_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_nn1.n3_test$probLeft/bst_dbf_db_tda_pca_5.40.5_nn1.n3_test$probRight
bst_dbf_db_tda_pca_5.40.5_nn1.n3_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_nn1.n3_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n3_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_nn1.n3_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1619667
## 
## $winRight
## [1] 0.8380333
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_nn1.n3_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n3_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_nn1.n3_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_nn1.n3_test)))

#BayesFactor
#bf_tda_pca_5.40.5_nn1.n3_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n3_test)) #bf_tda_pca_5.40.5_nn1.n3_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n3_test))


##Node4

#Neural Network 1
DryBean_TDA_PC_5.40.5_n4_NN1Fit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.40.5.n4.vec, 
                           Importance = T,
                     method = 'nnet', 
                     trControl = fitControl,
                     tuneGrid = nn1Grid,
                     metric='Accuracy')
## # weights:  46
## initial  value 781.157386 
## iter  10 value 654.412722
## iter  20 value 591.952853
## iter  30 value 375.320398
## iter  40 value 325.363131
## iter  50 value 298.583475
## iter  60 value 284.124123
## iter  70 value 274.209218
## iter  80 value 264.162752
## iter  90 value 263.003243
## iter 100 value 256.967497
## final  value 256.967497 
## stopped after 100 iterations
## # weights:  67
## initial  value 907.168450 
## iter  10 value 648.555299
## iter  20 value 574.230526
## iter  30 value 301.103669
## iter  40 value 268.055062
## iter  50 value 266.760695
## iter  60 value 265.822232
## iter  70 value 265.599352
## iter  80 value 265.546500
## iter  90 value 265.487927
## iter 100 value 265.196699
## final  value 265.196699 
## stopped after 100 iterations
## # weights:  109
## initial  value 1365.078957 
## iter  10 value 653.988816
## iter  20 value 653.440361
## iter  30 value 638.935823
## iter  40 value 474.244932
## iter  50 value 318.516541
## iter  60 value 306.349529
## iter  70 value 304.301438
## iter  80 value 245.673136
## iter  90 value 223.920005
## iter 100 value 167.569448
## final  value 167.569448 
## stopped after 100 iterations
## # weights:  151
## initial  value 1050.141679 
## iter  10 value 646.104367
## iter  20 value 637.644690
## iter  30 value 459.431642
## iter  40 value 284.664088
## iter  50 value 245.420537
## iter  60 value 208.410366
## iter  70 value 207.249712
## iter  80 value 135.164900
## iter  90 value 97.563198
## iter 100 value 78.631712
## final  value 78.631712 
## stopped after 100 iterations
## # weights:  46
## initial  value 1101.276292 
## iter  10 value 688.641476
## iter  20 value 675.225731
## iter  30 value 654.197217
## iter  40 value 654.074042
## iter  40 value 654.074042
## iter  50 value 649.967167
## iter  60 value 612.921092
## iter  70 value 293.674058
## iter  80 value 283.686735
## iter  90 value 277.829844
## iter 100 value 275.980094
## final  value 275.980094 
## stopped after 100 iterations
## # weights:  67
## initial  value 931.985092 
## iter  10 value 656.073239
## iter  20 value 615.457111
## iter  30 value 546.329696
## iter  40 value 313.466434
## iter  50 value 240.930041
## iter  60 value 216.369174
## iter  70 value 183.530840
## iter  80 value 172.173994
## iter  90 value 157.730702
## iter 100 value 147.977372
## final  value 147.977372 
## stopped after 100 iterations
## # weights:  109
## initial  value 1104.709671 
## iter  10 value 654.234156
## iter  20 value 653.533245
## iter  30 value 653.370652
## iter  40 value 290.148660
## iter  50 value 278.067637
## iter  60 value 277.022654
## iter  70 value 275.650572
## iter  80 value 274.520967
## iter  90 value 270.134976
## iter 100 value 231.120309
## final  value 231.120309 
## stopped after 100 iterations
## # weights:  151
## initial  value 953.734340 
## iter  10 value 656.430303
## iter  20 value 653.569153
## iter  30 value 653.525547
## iter  40 value 653.519440
## iter  50 value 597.958968
## iter  60 value 415.497595
## iter  70 value 394.188525
## iter  80 value 257.211797
## iter  90 value 228.344332
## iter 100 value 197.928919
## final  value 197.928919 
## stopped after 100 iterations
## # weights:  46
## initial  value 1143.775866 
## iter  10 value 658.594307
## iter  20 value 657.652773
## iter  30 value 579.722882
## iter  40 value 413.633549
## iter  50 value 345.128486
## iter  60 value 339.192012
## iter  70 value 301.684734
## iter  80 value 288.634144
## iter  90 value 284.539320
## iter 100 value 284.328989
## final  value 284.328989 
## stopped after 100 iterations
## # weights:  67
## initial  value 1307.641588 
## iter  10 value 648.248896
## iter  20 value 527.353269
## iter  30 value 337.408655
## iter  40 value 316.085450
## iter  50 value 292.952961
## iter  60 value 271.602592
## iter  70 value 267.935440
## iter  80 value 267.016746
## iter  90 value 263.231093
## iter 100 value 241.684187
## final  value 241.684187 
## stopped after 100 iterations
## # weights:  109
## initial  value 1397.838360 
## iter  10 value 653.671213
## iter  20 value 632.878395
## iter  30 value 629.545679
## iter  40 value 610.451349
## iter  50 value 600.127652
## iter  60 value 314.748352
## iter  70 value 278.615800
## iter  80 value 244.671665
## iter  90 value 164.762337
## iter 100 value 150.156142
## final  value 150.156142 
## stopped after 100 iterations
## # weights:  151
## initial  value 818.685765 
## iter  10 value 653.531510
## iter  20 value 644.098154
## iter  30 value 348.754380
## iter  40 value 318.586546
## iter  50 value 288.722486
## iter  60 value 287.545831
## iter  70 value 285.624005
## iter  80 value 282.497880
## iter  90 value 261.241913
## iter 100 value 165.308325
## final  value 165.308325 
## stopped after 100 iterations
## # weights:  46
## initial  value 795.083471 
## iter  10 value 659.544522
## iter  20 value 646.861976
## iter  30 value 313.458329
## iter  40 value 281.636427
## iter  50 value 260.226091
## iter  60 value 254.289676
## iter  70 value 191.992197
## iter  80 value 148.240110
## iter  90 value 139.572588
## iter 100 value 134.501064
## final  value 134.501064 
## stopped after 100 iterations
## # weights:  67
## initial  value 751.541902 
## iter  10 value 658.615326
## iter  20 value 574.022033
## iter  30 value 494.187262
## iter  40 value 319.160804
## iter  50 value 300.723043
## iter  60 value 277.031022
## iter  70 value 268.462521
## iter  80 value 268.122035
## iter  90 value 264.523726
## iter 100 value 239.356538
## final  value 239.356538 
## stopped after 100 iterations
## # weights:  109
## initial  value 1215.672787 
## iter  10 value 660.197715
## iter  20 value 658.483317
## iter  30 value 644.142280
## iter  40 value 512.504107
## iter  50 value 368.632544
## iter  60 value 282.240828
## iter  70 value 273.263260
## iter  80 value 268.740127
## iter  90 value 268.575761
## iter 100 value 267.906098
## final  value 267.906098 
## stopped after 100 iterations
## # weights:  151
## initial  value 712.897442 
## iter  10 value 648.468413
## iter  20 value 629.054041
## iter  30 value 535.903067
## iter  40 value 270.866093
## iter  50 value 260.358704
## iter  60 value 249.826802
## iter  70 value 205.579921
## iter  80 value 157.151883
## iter  90 value 144.672671
## iter 100 value 113.056865
## final  value 113.056865 
## stopped after 100 iterations
## # weights:  46
## initial  value 866.586296 
## iter  10 value 659.890443
## iter  20 value 563.409043
## iter  30 value 348.801294
## iter  40 value 290.148868
## iter  50 value 281.688208
## iter  60 value 281.465952
## iter  70 value 276.641736
## iter  80 value 225.426662
## iter  90 value 222.539671
## iter 100 value 220.810919
## final  value 220.810919 
## stopped after 100 iterations
## # weights:  67
## initial  value 844.945264 
## iter  10 value 684.363932
## iter  20 value 659.220709
## iter  30 value 658.819421
## iter  40 value 646.148585
## iter  50 value 458.699443
## iter  60 value 411.758156
## iter  70 value 329.952763
## iter  80 value 255.909228
## iter  90 value 188.539691
## iter 100 value 148.631042
## final  value 148.631042 
## stopped after 100 iterations
## # weights:  109
## initial  value 993.468303 
## iter  10 value 654.017584
## iter  20 value 636.207464
## iter  30 value 528.300413
## iter  40 value 361.683556
## iter  50 value 253.324241
## iter  60 value 208.924758
## iter  70 value 149.868566
## iter  80 value 138.479053
## iter  90 value 99.035585
## iter 100 value 88.274266
## final  value 88.274266 
## stopped after 100 iterations
## # weights:  151
## initial  value 856.575384 
## iter  10 value 647.699094
## iter  20 value 300.647900
## iter  30 value 294.425529
## iter  40 value 280.139703
## iter  50 value 275.921367
## iter  60 value 265.665717
## iter  70 value 202.310191
## iter  80 value 193.566093
## iter  90 value 164.581252
## iter 100 value 160.713848
## final  value 160.713848 
## stopped after 100 iterations
## # weights:  46
## initial  value 981.508380 
## iter  10 value 663.408478
## iter  20 value 660.785307
## iter  30 value 660.737144
## iter  40 value 637.757513
## iter  50 value 512.397313
## iter  60 value 509.814615
## iter  70 value 508.767835
## iter  80 value 368.988549
## iter  90 value 309.552581
## iter 100 value 293.947725
## final  value 293.947725 
## stopped after 100 iterations
## # weights:  67
## initial  value 771.316373 
## iter  10 value 650.347267
## iter  20 value 583.035491
## iter  30 value 420.603992
## iter  40 value 361.557860
## iter  50 value 258.650767
## iter  60 value 206.001787
## iter  70 value 166.459189
## iter  80 value 149.548267
## iter  90 value 143.732014
## iter 100 value 141.659373
## final  value 141.659373 
## stopped after 100 iterations
## # weights:  109
## initial  value 1158.184355 
## iter  10 value 659.186674
## iter  20 value 658.930322
## iter  30 value 643.995047
## iter  40 value 567.885089
## iter  50 value 358.973163
## iter  60 value 313.663443
## iter  70 value 293.343259
## iter  80 value 272.633874
## iter  90 value 255.498194
## iter 100 value 240.684671
## final  value 240.684671 
## stopped after 100 iterations
## # weights:  151
## initial  value 1028.734103 
## iter  10 value 661.923397
## iter  20 value 658.758899
## iter  30 value 658.603597
## iter  40 value 504.928801
## iter  50 value 344.712140
## iter  60 value 328.792636
## iter  70 value 285.917849
## iter  80 value 269.828373
## iter  90 value 246.019703
## iter 100 value 194.926350
## final  value 194.926350 
## stopped after 100 iterations
## # weights:  46
## initial  value 763.699539 
## final  value 653.225369 
## converged
## # weights:  67
## initial  value 838.012597 
## iter  10 value 654.991218
## iter  20 value 541.568159
## iter  30 value 401.609862
## iter  40 value 298.117248
## iter  50 value 266.473285
## iter  60 value 265.797011
## iter  70 value 265.110962
## iter  80 value 241.882884
## iter  90 value 182.898320
## iter 100 value 148.071596
## final  value 148.071596 
## stopped after 100 iterations
## # weights:  109
## initial  value 1257.796092 
## iter  10 value 652.572999
## iter  20 value 509.035273
## iter  30 value 508.714964
## iter  40 value 504.839660
## iter  50 value 318.430294
## iter  60 value 264.413525
## iter  70 value 171.266460
## iter  80 value 163.395858
## iter  90 value 146.720494
## iter 100 value 133.208939
## final  value 133.208939 
## stopped after 100 iterations
## # weights:  151
## initial  value 828.918008 
## iter  10 value 655.989143
## iter  20 value 631.892938
## iter  30 value 465.563160
## iter  40 value 361.115757
## iter  50 value 277.224146
## iter  60 value 272.741374
## iter  70 value 271.558635
## iter  80 value 236.288703
## iter  90 value 203.212748
## iter 100 value 185.324471
## final  value 185.324471 
## stopped after 100 iterations
## # weights:  46
## initial  value 759.188248 
## iter  10 value 653.684583
## iter  20 value 652.296406
## iter  30 value 542.344576
## iter  40 value 513.564029
## iter  50 value 384.074645
## iter  60 value 282.355380
## iter  70 value 276.575271
## iter  80 value 263.446295
## iter  90 value 234.478349
## iter 100 value 222.132520
## final  value 222.132520 
## stopped after 100 iterations
## # weights:  67
## initial  value 1118.885603 
## iter  10 value 659.171982
## iter  20 value 567.527620
## iter  30 value 422.470931
## iter  40 value 394.427625
## iter  50 value 355.480646
## iter  60 value 238.367271
## iter  70 value 196.141435
## iter  80 value 158.137856
## iter  90 value 133.416837
## iter 100 value 120.154678
## final  value 120.154678 
## stopped after 100 iterations
## # weights:  109
## initial  value 802.164724 
## iter  10 value 655.141020
## iter  20 value 649.616439
## iter  30 value 633.360361
## iter  40 value 388.019544
## iter  50 value 307.132048
## iter  60 value 267.765039
## iter  70 value 246.914022
## iter  80 value 238.634911
## iter  90 value 182.678495
## iter 100 value 144.952216
## final  value 144.952216 
## stopped after 100 iterations
## # weights:  151
## initial  value 874.032104 
## iter  10 value 654.861514
## iter  20 value 652.764566
## iter  30 value 652.737924
## iter  40 value 564.737189
## iter  50 value 478.174278
## iter  60 value 462.872295
## iter  70 value 440.247379
## iter  80 value 411.834541
## iter  90 value 387.822849
## iter 100 value 328.986647
## final  value 328.986647 
## stopped after 100 iterations
## # weights:  46
## initial  value 1115.413357 
## iter  10 value 663.103037
## iter  20 value 653.708330
## iter  30 value 542.214355
## iter  40 value 336.138211
## iter  50 value 297.752939
## iter  60 value 279.893162
## iter  70 value 268.083520
## iter  80 value 267.867946
## iter  90 value 266.440864
## iter 100 value 243.410843
## final  value 243.410843 
## stopped after 100 iterations
## # weights:  67
## initial  value 811.658899 
## iter  10 value 653.643135
## iter  20 value 652.990403
## iter  30 value 647.873389
## iter  40 value 547.400186
## iter  50 value 402.828254
## iter  60 value 337.334876
## iter  70 value 302.449320
## iter  80 value 253.482106
## iter  90 value 197.830914
## iter 100 value 191.410366
## final  value 191.410366 
## stopped after 100 iterations
## # weights:  109
## initial  value 1007.591263 
## iter  10 value 657.529909
## iter  20 value 639.646118
## iter  30 value 424.205720
## iter  40 value 308.469441
## iter  50 value 299.248197
## iter  60 value 241.229258
## iter  70 value 225.121634
## iter  80 value 196.732321
## iter  90 value 176.618296
## iter 100 value 167.338893
## final  value 167.338893 
## stopped after 100 iterations
## # weights:  151
## initial  value 716.404624 
## iter  10 value 652.645786
## iter  20 value 624.818298
## iter  30 value 486.714115
## iter  40 value 359.417442
## iter  50 value 313.721126
## iter  60 value 234.992126
## iter  70 value 183.050773
## iter  80 value 170.151479
## iter  90 value 155.518698
## iter 100 value 149.368703
## final  value 149.368703 
## stopped after 100 iterations
## # weights:  67
## initial  value 1248.942473 
## iter  10 value 984.008221
## iter  20 value 983.641457
## iter  30 value 887.582069
## iter  40 value 851.632627
## iter  50 value 771.355257
## iter  60 value 661.216247
## iter  70 value 596.925053
## iter  80 value 561.779733
## iter  90 value 509.101360
## iter 100 value 459.391159
## final  value 459.391159 
## stopped after 100 iterations
DryBean_TDA_PC_5.40.5_n4_NN1Fit0
## Neural Network 
## 
## 894 samples
##  16 predictor
##   4 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'HOROZ' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 595, 598, 595 
## Resampling results across tuning parameters:
## 
##   size  decay  Accuracy   Kappa    
##   2     0.3    0.7623497  0.5416868
##   2     0.5    0.8579989  0.7611538
##   2     0.7    0.8378190  0.7201753
##   3     0.3    0.8734935  0.7831060
##   3     0.5    0.9542017  0.9253396
##   3     0.7    0.9196421  0.8642356
##   5     0.3    0.9102828  0.8478730
##   5     0.5    0.9330426  0.8922783
##   5     0.7    0.9046973  0.8386499
##   7     0.3    0.9340444  0.8928238
##   7     0.5    0.9027841  0.8357405
##   7     0.7    0.9351479  0.8927513
## 
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 3 and decay = 0.5.
DryBean_TDA_PC_5.40.5_n4_NN1Fit0$resample
##    Accuracy     Kappa Resample
## 1 0.9464883 0.9138530    Fold3
## 2 0.9729730 0.9559393    Fold2
## 3 0.9431438 0.9062264    Fold1
db_tda_pc_5.40.5_n4_nn1_fit_re<-DryBean_TDA_PC_5.40.5_n4_NN1Fit0$resample[1]

summary(DryBean_TDA_PC_5.40.5_n4_NN1Fit0)
## a 16-3-4 network with 67 weights
## options were - softmax modelling  decay=0.5
##   b->h1  i1->h1  i2->h1  i3->h1  i4->h1  i5->h1  i6->h1  i7->h1  i8->h1  i9->h1 
##   -0.01    0.00   -0.63   -0.25   -0.49   -0.05   -0.01    0.00    2.70    0.07 
## i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1 i16->h1 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.01 
##   b->h2  i1->h2  i2->h2  i3->h2  i4->h2  i5->h2  i6->h2  i7->h2  i8->h2  i9->h2 
##    0.02    0.33    0.07   -0.71    0.57    0.05    0.01   -0.30   -1.13   -0.04 
## i10->h2 i11->h2 i12->h2 i13->h2 i14->h2 i15->h2 i16->h2 
##    0.02    0.00    0.01    0.00    0.00    0.00    0.01 
##   b->h3  i1->h3  i2->h3  i3->h3  i4->h3  i5->h3  i6->h3  i7->h3  i8->h3  i9->h3 
##    0.02   -0.09    0.39    0.29   -0.04    0.06    0.02    0.08   -1.04    0.04 
## i10->h3 i11->h3 i12->h3 i13->h3 i14->h3 i15->h3 i16->h3 
##    0.02    0.01    0.01    0.00    0.00    0.01    0.02 
##  b->o1 h1->o1 h2->o1 h3->o1 
##  -0.76  -3.62   1.88  -0.08 
##  b->o2 h1->o2 h2->o2 h3->o2 
##   0.81   3.71  -1.00  -2.45 
##  b->o3 h1->o3 h2->o3 h3->o3 
##   0.00   3.00  -1.41   1.15 
##  b->o4 h1->o4 h2->o4 h3->o4 
##  -0.05  -3.08   0.53   1.38
#vip(DryBean_TDA_PC_5.40.5_n4_NN1Fit0,25) + ggtitle("dryBean_TDA_PCA_5.40.5_n4_NN1Fit TDA-Assited NN")

# Predict outcome using DryBean_TDA_PC_5.40.5_n4_NN1Fit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.40.5_n4_NN1Fit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.40.5_n4_db_nn1_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length

## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
db_tda_pc_5.40.5_n4_db_nn1_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0    130    6        0     0     2    0
##   CALI          210     26  470     1027    98   590  771
##   DERMASON        0      0    0        0     0     0    0
##   HOROZ         186      0   13       36   480    16   19
##   SEKER           0      0    0        0     0     0    0
##   SIRA            0      0    0        0     0     0    0
## 
## Overall Statistics
##                                           
##                Accuracy : 0.2647          
##                  95% CI : (0.2512, 0.2785)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : 0.2775          
##                                           
##                   Kappa : 0.1634          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.83333      0.9611          0.0000
## Specificity                  1.00000       0.99796      0.2420          1.0000
## Pos Pred Value                   NaN       0.94203      0.1472             NaN
## Neg Pred Value               0.90294       0.99340      0.9786          0.7395
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.00000       0.03186      0.1152          0.0000
## Detection Prevalence         0.00000       0.03382      0.7824          0.0000
## Balanced Accuracy            0.50000       0.91565      0.6016          0.5000
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.8304        0.000      0.0000
## Specificity                0.9229        1.000      1.0000
## Pos Pred Value             0.6400          NaN         NaN
## Neg Pred Value             0.9706        0.851      0.8064
## Prevalence                 0.1417        0.149      0.1936
## Detection Rate             0.1176        0.000      0.0000
## Detection Prevalence       0.1838        0.000      0.0000
## Balanced Accuracy          0.8767        0.500      0.5000
db_tda_pc_5.40.5_n4_db_nn1_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0    130    6        0     0     2    0
##   CALI          210     26  470     1027    98   590  771
##   DERMASON        0      0    0        0     0     0    0
##   HOROZ         186      0   13       36   480    16   19
##   SEKER           0      0    0        0     0     0    0
##   SIRA            0      0    0        0     0     0    0
## 
## Overall Statistics
##                                           
##                Accuracy : 0.2647          
##                  95% CI : (0.2512, 0.2785)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : 0.2775          
##                                           
##                   Kappa : 0.1634          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.83333      0.9611          0.0000
## Specificity                  1.00000       0.99796      0.2420          1.0000
## Pos Pred Value                   NaN       0.94203      0.1472             NaN
## Neg Pred Value               0.90294       0.99340      0.9786          0.7395
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.00000       0.03186      0.1152          0.0000
## Detection Prevalence         0.00000       0.03382      0.7824          0.0000
## Balanced Accuracy            0.50000       0.91565      0.6016          0.5000
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.8304        0.000      0.0000
## Specificity                0.9229        1.000      1.0000
## Pos Pred Value             0.6400          NaN         NaN
## Neg Pred Value             0.9706        0.851      0.8064
## Prevalence                 0.1417        0.149      0.1936
## Detection Rate             0.1176        0.000      0.0000
## Detection Prevalence       0.1838        0.000      0.0000
## Balanced Accuracy          0.8767        0.500      0.5000
db_tda_pc_5.40.5_n4_db_nn1_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.2647059      0.1633906      0.2512184      0.2785310      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.2774710            NaN
db_tda_pc_5.40.5_n4_db_nn1_cf0_ov_acc<-db_tda_pc_5.40.5_n4_db_nn1_cf0$overall[1]
db_tda_pc_5.40.5_n4_db_nn1_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.0000000   1.0000000            NaN      0.9029412        NA
## Class: BOMBAY     0.8333333   0.9979613      0.9420290      0.9934044 0.9420290
## Class: CALI       0.9611452   0.2419939      0.1472431      0.9786036 0.1472431
## Class: DERMASON   0.0000000   1.0000000            NaN      0.7394608        NA
## Class: HOROZ      0.8304498   0.9229012      0.6400000      0.9705706 0.6400000
## Class: SEKER      0.0000000   1.0000000            NaN      0.8509804        NA
## Class: SIRA       0.0000000   1.0000000            NaN      0.8063725        NA
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.0000000        NA 0.09705882     0.00000000
## Class: BOMBAY   0.8333333 0.8843537 0.03823529     0.03186275
## Class: CALI     0.9611452 0.2553654 0.11985294     0.11519608
## Class: DERMASON 0.0000000        NA 0.26053922     0.00000000
## Class: HOROZ    0.8304498 0.7228916 0.14166667     0.11764706
## Class: SEKER    0.0000000        NA 0.14901961     0.00000000
## Class: SIRA     0.0000000        NA 0.19362745     0.00000000
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA           0.00000000         0.5000000
## Class: BOMBAY             0.03382353         0.9156473
## Class: CALI               0.78235294         0.6015695
## Class: DERMASON           0.00000000         0.5000000
## Class: HOROZ              0.18382353         0.8766755
## Class: SEKER              0.00000000         0.5000000
## Class: SIRA               0.00000000         0.5000000
db_tda_pc_5.40.5_n4_db_nn1_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n4_db_nn1_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_pca_5.40.5_nn1_n4_3_fold<-(db_nn1_fit_re - db_tda_pc_5.40.5_n4_nn1_fit_re)
diff_drybean_tda_pca_5.40.5_nn1_n4_3_fold
##     Accuracy
## 1 -0.4721414
## 2 -0.2335962
## 3 -0.6828353
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_nn1.n4_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n4_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_nn1.n4_3_fold
## $probLeft
## [1] 0.75
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_nn1.n4_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_nn1.n4_3_fold$probLeft/bst_dbf_db_tda_pca_5.40.5_nn1.n4_3_fold$probRight
bst_dbf_db_tda_pca_5.40.5_nn1.n4_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_nn1.n4_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n4_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_nn1.n4_3_fold
## $winLeft
## [1] 0.9909333
## 
## $winRope
## [1] 0.009066667
## 
## $winRight
## [1] 0
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_nn1.n4_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n4_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_nn1.n4_3_fold
## $left
## [1] 0.9528708
## 
## $rope
## [1] 0.003406066
## 
## $right
## [1] 0.04372312
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.40.5_nn1_n4_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_nn1.n4_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n4_3_fold))
#bf_tda_pca_5.40.5_nn1.n4_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n4_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n4_3_fold)
## t = -3.5668, df = 2, p-value = 0.0704
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -1.02120094  0.09548562
## sample estimates:
##  mean of x 
## -0.4628577
### Test set diff
diff_drybean_tda_pca_5.40.5_nn1.n4_test<-(db_nn1_cf_ov_acc - db_tda_pc_5.40.5_n4_db_nn1_cf0_ov_acc)
diff_drybean_tda_pca_5.40.5_nn1.n4_test
##  Accuracy 
## 0.1132353
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_nn1.n4_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n4_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_nn1.n4_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_nn1.n4_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_nn1.n4_test$probLeft/bst_dbf_db_tda_pca_5.40.5_nn1.n4_test$probRight
bst_dbf_db_tda_pca_5.40.5_nn1.n4_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_nn1.n4_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n4_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_nn1.n4_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1556
## 
## $winRight
## [1] 0.8444
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_nn1.n4_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n4_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_nn1.n4_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_nn1.n4_test)))

#BayesFactor
#bf_tda_pca_5.40.5_nn1.n4_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n4_test)) #bf_tda_pca_5.40.5_nn1.n4_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n4_test))


##Node5

#Neural Network 1
#DryBean_TDA_PC_5.40.5_n5_NN1Fit0 <- train(as.factor(Class) ~ ., data = #tda.m_dry_bean_dataset_5.40.5.n5.vec, 
#                          Importance = T,
#                     method = 'nnet', 
#                     trControl = fitControl,
#                     tuneGrid = nn1Grid,
#                     metric='Accuracy')

#DryBean_TDA_PC_5.40.5_n5_NN1Fit0
#DryBean_TDA_PC_5.40.5_n5_NN1Fit0$resample
#db_tda_pc_5.40.5_n5_nn1_fit_re<-DryBean_TDA_PC_5.40.5_n5_NN1Fit0$resample[1]

#summary(DryBean_TDA_PC_5.40.5_n5_NN1Fit0)

#vip(DryBean_TDA_PC_5.40.5_n5_NN1Fit0,25) + ggtitle("dryBean_TDA_PCA_5.40.5_n5_NN1Fit TDA-Assited NN")

# Predict outcome using DryBean_TDA_PC_5.40.5_n5_NN1Fit0 from training data based on testing data
#pred0 <- predict(DryBean_TDA_PC_5.40.5_n5_NN1Fit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
#db_tda_pc_5.40.5_n5_db_nn1_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
#db_tda_pc_5.40.5_n5_db_nn1_cf0
#db_tda_pc_5.40.5_n5_db_nn1_cf0 
#db_tda_pc_5.40.5_n5_db_nn1_cf0$overall
#db_tda_pc_5.40.5_n5_db_nn1_cf0_ov_acc<-db_tda_pc_5.40.5_n5_db_nn1_cf0$overall[1]
#db_tda_pc_5.40.5_n5_db_nn1_cf0$byClass
#db_tda_pc_5.40.5_n5_db_nn1_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n5_db_nn1_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

#diff_drybean_tda_pca_5.40.5_nn1_n5_3_fold<-(db_nn1_fit_re - db_tda_pc_5.40.5_n5_nn1_fit_re)
#diff_drybean_tda_pca_5.40.5_nn1_n5_3_fold#

## Bayesian Tests 3-fold diff

# Bayesian Sign Test

#bst_dbf_db_tda_pca_5.40.5_nn1.n5_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n5_3_fold),-0.01,0.01)
#bst_dbf_db_tda_pca_5.40.5_nn1.n5_3_fold

# Odds Left Bayesian Sign Test 

#bst_dbf_db_tda_pca_5.40.5_nn1.n5_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_nn1.n5_3_fold$probLeft/bst_dbf_db_tda_pca_5.40.5_nn1.n5_3_fold$probRight
#bst_dbf_db_tda_pca_5.40.5_nn1.n5_3_fold_odds.left

# Bayesian Signed Rank Test

#bsr_dbf_db_tda_pca_5.40.5_nn1.n5_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n5_3_fold),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.40.5_nn1.n5_3_fold

# Bayesian Correlated Test

#bct_dbf_db_tda_pca_5.40.5_nn1.n5_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n5_3_fold),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.40.5_nn1.n5_3_fold

# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_nn1_n5_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_nn1.n5_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n5_3_fold))
#bf_tda_pca_5.40.5_nn1.n5_3_fold

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_nn1_n5_3_fold))


### Test set diff
#diff_drybean_tda_pca_5.40.5_nn1.n5_test<-(db_nn1_cf_ov_acc - db_tda_pc_5.40.5_n5_db_nn1_cf0_ov_acc)
#diff_drybean_tda_pca_5.40.5_nn1.n5_test


## Bayesian Tests Test set diff

# Bayesian Sign Test

#bst_dbf_db_tda_pca_5.40.5_nn1.n5_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n5_test),-0.01,0.01)
#bst_dbf_db_tda_pca_5.40.5_nn1.n5_test

# Odds Left Bayesian Sign Test 

#bst_dbf_db_tda_pca_5.40.5_nn1.n5_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_nn1.n5_test$probLeft/bst_dbf_db_tda_pca_5.40.5_nn1.n5_test$probRight
#bst_dbf_db_tda_pca_5.40.5_nn1.n5_test_odds.left

# Bayesian Signed Rank Test

#bsr_dbf_db_tda_pca_5.40.5_nn1.n5_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n5_test),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.40.5_nn1.n5_test

# Bayesian Correlated Test

#bct_dbf_db_tda_pca_5.40.5_nn1.n5_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n5_test),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.40.5_nn1.n5_test

# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_nn1.n5_test)))

#BayesFactor
#bf_tda_pca_5.40.5_nn1.n5_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n5_test)) #bf_tda_pca_5.40.5_nn1.n5_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_nn1.n5_test))


##With TDA KDE filter 5 intervals, 50% overlap, 5 bins 
##Node1

#Neural Network 1
DryBean_TDA_KDE_5.40.5_n1_NN1Fit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.40.5.n1.vec, 
                           Importance = T,
                     method = 'nnet', 
                     trControl = fitControl,
                     tuneGrid = nn1Grid,
                     metric='Accuracy')
## # weights:  55
## initial  value 10585.562703 
## iter  10 value 9243.833776
## iter  20 value 9177.635965
## iter  30 value 8417.773326
## iter  40 value 8195.514763
## iter  50 value 7951.440958
## iter  60 value 7055.157469
## iter  70 value 6127.570515
## iter  80 value 5739.581681
## iter  90 value 5672.221809
## iter 100 value 5420.015591
## final  value 5420.015591 
## stopped after 100 iterations
## # weights:  79
## initial  value 10199.465953 
## iter  10 value 9243.953741
## iter  20 value 7771.385814
## iter  30 value 7370.509658
## iter  40 value 7308.863084
## iter  50 value 7243.977408
## iter  60 value 7191.245760
## iter  70 value 6181.412428
## iter  80 value 5472.108244
## iter  90 value 5187.068724
## iter 100 value 4931.517621
## final  value 4931.517621 
## stopped after 100 iterations
## # weights:  127
## initial  value 12781.211482 
## iter  10 value 9273.271830
## iter  20 value 9239.971477
## iter  30 value 9184.556374
## iter  40 value 9166.290471
## iter  50 value 9135.410758
## iter  60 value 8907.410238
## iter  70 value 7673.103893
## iter  80 value 6358.522241
## iter  90 value 6206.225107
## iter 100 value 6186.159157
## final  value 6186.159157 
## stopped after 100 iterations
## # weights:  175
## initial  value 11536.263183 
## iter  10 value 9243.591291
## iter  20 value 8881.827629
## iter  30 value 7343.135497
## iter  40 value 7291.948877
## iter  50 value 7267.557266
## iter  60 value 7250.939480
## iter  70 value 7234.278791
## iter  80 value 7202.984449
## iter  90 value 6987.623739
## iter 100 value 6821.632470
## final  value 6821.632470 
## stopped after 100 iterations
## # weights:  55
## initial  value 10278.310818 
## iter  10 value 9331.408271
## iter  20 value 9246.681862
## iter  30 value 9244.746387
## iter  40 value 9244.448467
## iter  50 value 9243.904020
## iter  50 value 9243.903962
## iter  60 value 9240.407558
## iter  70 value 9044.303588
## iter  80 value 8430.171911
## iter  90 value 7877.381308
## iter 100 value 7435.571073
## final  value 7435.571073 
## stopped after 100 iterations
## # weights:  79
## initial  value 10830.721881 
## iter  10 value 9247.626938
## iter  20 value 9243.780273
## iter  30 value 9236.315342
## iter  40 value 7798.363363
## iter  50 value 7173.540952
## iter  60 value 6387.116180
## iter  70 value 5979.092516
## iter  80 value 5842.617630
## iter  90 value 5793.231485
## iter 100 value 5748.754883
## final  value 5748.754883 
## stopped after 100 iterations
## # weights:  127
## initial  value 9977.561327 
## iter  10 value 9254.136735
## iter  20 value 9243.859238
## iter  30 value 9243.735575
## iter  40 value 9145.240083
## iter  50 value 8118.779482
## iter  60 value 8068.079122
## iter  70 value 8009.232937
## iter  80 value 7352.553402
## iter  90 value 6344.220927
## iter 100 value 5999.928308
## final  value 5999.928308 
## stopped after 100 iterations
## # weights:  175
## initial  value 10438.478338 
## iter  10 value 9260.587188
## iter  20 value 9243.960745
## final  value 9243.741333 
## converged
## # weights:  55
## initial  value 10753.627629 
## iter  10 value 9244.287717
## final  value 9244.022196 
## converged
## # weights:  79
## initial  value 10301.792744 
## iter  10 value 9246.632686
## iter  20 value 9244.437539
## iter  30 value 9239.437812
## iter  40 value 8472.823082
## iter  50 value 8119.299548
## iter  60 value 7326.935494
## iter  70 value 6226.239079
## iter  80 value 5859.888646
## iter  90 value 5780.875178
## iter 100 value 5488.992267
## final  value 5488.992267 
## stopped after 100 iterations
## # weights:  127
## initial  value 10325.860926 
## iter  10 value 9243.708253
## iter  20 value 8161.406316
## iter  30 value 8011.060747
## iter  40 value 7414.892967
## iter  50 value 7214.446555
## iter  60 value 6919.544039
## iter  70 value 6504.412273
## iter  80 value 5208.458192
## iter  90 value 4554.375641
## iter 100 value 4295.671482
## final  value 4295.671482 
## stopped after 100 iterations
## # weights:  175
## initial  value 10481.231429 
## iter  10 value 9243.950058
## iter  20 value 9243.235180
## iter  30 value 7878.243903
## iter  40 value 7585.861855
## iter  50 value 7449.356600
## iter  60 value 7165.466169
## iter  70 value 7064.501119
## iter  80 value 6646.431719
## iter  90 value 6185.506379
## iter 100 value 5958.743628
## final  value 5958.743628 
## stopped after 100 iterations
## # weights:  55
## initial  value 9997.039550 
## iter  10 value 9242.516244
## iter  20 value 9237.691014
## iter  30 value 7893.925411
## iter  40 value 7453.661930
## iter  50 value 7238.923286
## iter  60 value 7125.132212
## iter  70 value 6874.524173
## iter  80 value 6657.633681
## iter  90 value 6614.592917
## iter 100 value 6471.195515
## final  value 6471.195515 
## stopped after 100 iterations
## # weights:  79
## initial  value 10143.553731 
## iter  10 value 9295.111868
## iter  20 value 9236.909930
## iter  30 value 8840.476729
## iter  40 value 8705.477320
## iter  50 value 8421.848559
## iter  60 value 7505.702885
## iter  70 value 6606.201702
## iter  80 value 6398.487098
## iter  90 value 6359.300043
## iter 100 value 6299.480150
## final  value 6299.480150 
## stopped after 100 iterations
## # weights:  127
## initial  value 11074.180260 
## iter  10 value 9729.610440
## iter  20 value 9712.099399
## iter  30 value 8169.957934
## iter  40 value 7571.659338
## iter  50 value 7448.593265
## iter  60 value 7283.380214
## iter  70 value 6947.880512
## iter  80 value 6815.772773
## iter  90 value 6650.489040
## iter 100 value 6584.255823
## final  value 6584.255823 
## stopped after 100 iterations
## # weights:  175
## initial  value 10373.654416 
## iter  10 value 9242.703839
## iter  20 value 9231.504684
## iter  30 value 8558.878111
## iter  40 value 8166.859172
## iter  50 value 7896.385501
## iter  60 value 7304.853365
## iter  70 value 6985.140342
## iter  80 value 6914.284414
## iter  90 value 6316.429700
## iter 100 value 6181.543758
## final  value 6181.543758 
## stopped after 100 iterations
## # weights:  55
## initial  value 9655.754509 
## iter  10 value 9246.011303
## iter  20 value 9242.664546
## iter  30 value 9237.031029
## iter  40 value 8392.445580
## iter  50 value 7896.700913
## iter  60 value 6833.088390
## iter  70 value 6729.197016
## iter  80 value 6705.575260
## iter  90 value 6702.284688
## iter 100 value 6571.489269
## final  value 6571.489269 
## stopped after 100 iterations
## # weights:  79
## initial  value 10988.201516 
## iter  10 value 9551.362590
## iter  20 value 9262.593520
## iter  30 value 9245.956189
## iter  40 value 9037.438916
## iter  50 value 8446.929901
## iter  60 value 8158.526849
## iter  70 value 8083.610592
## iter  80 value 8058.912832
## iter  90 value 8043.583691
## iter 100 value 8040.777267
## final  value 8040.777267 
## stopped after 100 iterations
## # weights:  127
## initial  value 11098.828800 
## iter  10 value 9244.383226
## iter  20 value 9242.449476
## iter  30 value 9241.650871
## iter  40 value 7766.903580
## iter  50 value 7283.094254
## iter  60 value 7163.709914
## iter  70 value 6428.563790
## iter  80 value 6019.318329
## iter  90 value 5459.395190
## iter 100 value 5216.632015
## final  value 5216.632015 
## stopped after 100 iterations
## # weights:  175
## initial  value 9877.962213 
## iter  10 value 9247.724592
## iter  20 value 9242.682541
## iter  30 value 9233.694736
## iter  40 value 9055.988616
## iter  50 value 8455.595054
## iter  60 value 8289.303515
## iter  70 value 8140.786070
## iter  80 value 7685.198092
## iter  90 value 7036.096013
## iter 100 value 6598.731366
## final  value 6598.731366 
## stopped after 100 iterations
## # weights:  55
## initial  value 9846.851744 
## iter  10 value 9243.343407
## iter  20 value 9242.616161
## final  value 9242.612871 
## converged
## # weights:  79
## initial  value 10238.125411 
## iter  10 value 9242.683575
## final  value 9242.597104 
## converged
## # weights:  127
## initial  value 10553.977082 
## iter  10 value 9243.193199
## iter  20 value 9242.461374
## iter  30 value 7873.407130
## iter  40 value 7598.961539
## iter  50 value 7434.776865
## iter  60 value 7393.204913
## iter  70 value 7376.256800
## iter  80 value 6905.352189
## iter  90 value 6005.114298
## iter 100 value 5629.520460
## final  value 5629.520460 
## stopped after 100 iterations
## # weights:  175
## initial  value 11757.991493 
## iter  10 value 9287.365494
## iter  20 value 7810.015892
## iter  30 value 7639.139465
## iter  40 value 7415.397997
## iter  50 value 7372.672693
## iter  60 value 7350.647648
## iter  70 value 7167.016826
## iter  80 value 5784.832115
## iter  90 value 5492.443641
## iter 100 value 4563.168568
## final  value 4563.168568 
## stopped after 100 iterations
## # weights:  55
## initial  value 9632.411203 
## iter  10 value 9246.690873
## iter  20 value 9246.506391
## iter  30 value 8515.381129
## iter  40 value 8461.409343
## iter  50 value 8365.546042
## iter  60 value 8259.839886
## iter  70 value 7232.335429
## iter  80 value 6928.645171
## iter  90 value 6836.297113
## iter 100 value 6834.781619
## final  value 6834.781619 
## stopped after 100 iterations
## # weights:  79
## initial  value 11031.357429 
## iter  10 value 9246.373850
## iter  20 value 9234.742851
## iter  30 value 7280.645479
## iter  40 value 7096.070497
## iter  50 value 6933.489205
## iter  60 value 6207.672540
## iter  70 value 6119.151877
## iter  80 value 6064.528328
## iter  90 value 6044.990752
## iter 100 value 6019.320393
## final  value 6019.320393 
## stopped after 100 iterations
## # weights:  127
## initial  value 9957.863803 
## iter  10 value 9246.694089
## final  value 9246.687773 
## converged
## # weights:  175
## initial  value 10868.449178 
## iter  10 value 9246.347433
## iter  20 value 9237.889125
## iter  30 value 7533.043731
## iter  40 value 7355.839400
## iter  50 value 7216.139951
## iter  60 value 7086.110085
## iter  70 value 6950.454010
## iter  80 value 6793.206472
## iter  90 value 6698.339645
## iter 100 value 6143.653061
## final  value 6143.653061 
## stopped after 100 iterations
## # weights:  55
## initial  value 10090.729029 
## iter  10 value 9274.999344
## iter  20 value 9247.662778
## iter  30 value 9247.470721
## iter  40 value 9194.737756
## iter  50 value 8916.004287
## iter  60 value 8588.974671
## iter  70 value 8078.729412
## iter  80 value 7750.203784
## iter  90 value 7470.402486
## iter 100 value 7352.743088
## final  value 7352.743088 
## stopped after 100 iterations
## # weights:  79
## initial  value 9735.088142 
## iter  10 value 9250.384803
## iter  20 value 9233.976893
## iter  30 value 8407.707227
## iter  40 value 8250.677052
## iter  50 value 8166.611122
## iter  60 value 8130.682135
## iter  70 value 8054.213316
## iter  80 value 7638.227283
## iter  90 value 7218.854261
## iter 100 value 7157.121078
## final  value 7157.121078 
## stopped after 100 iterations
## # weights:  127
## initial  value 9997.784797 
## iter  10 value 9252.166159
## iter  20 value 9246.542869
## iter  30 value 9053.191469
## iter  40 value 8537.298796
## iter  50 value 7993.752171
## iter  60 value 6837.222256
## iter  70 value 6372.262642
## iter  80 value 5064.475560
## iter  90 value 4721.399528
## iter 100 value 4396.882132
## final  value 4396.882132 
## stopped after 100 iterations
## # weights:  175
## initial  value 11019.791150 
## iter  10 value 9279.337140
## iter  20 value 9029.655322
## iter  30 value 8658.410824
## iter  40 value 8042.662948
## iter  50 value 7357.767103
## iter  60 value 7032.694151
## iter  70 value 6854.233333
## iter  80 value 6194.189810
## iter  90 value 5289.319460
## iter 100 value 5031.060157
## final  value 5031.060157 
## stopped after 100 iterations
## # weights:  55
## initial  value 9933.402353 
## iter  10 value 9288.536087
## iter  20 value 9246.496959
## iter  30 value 8859.519710
## iter  40 value 7422.556217
## iter  50 value 6497.292165
## iter  60 value 5616.244138
## iter  70 value 5103.663946
## iter  80 value 5074.517220
## iter  90 value 4945.422026
## iter 100 value 4807.145476
## final  value 4807.145476 
## stopped after 100 iterations
## # weights:  79
## initial  value 10992.467583 
## iter  10 value 9248.381543
## iter  20 value 8963.710467
## iter  30 value 8930.758283
## iter  40 value 8160.452387
## iter  50 value 7527.220755
## iter  60 value 7434.798100
## iter  70 value 7055.202016
## iter  80 value 7009.615271
## iter  90 value 6488.774002
## iter 100 value 6096.890983
## final  value 6096.890983 
## stopped after 100 iterations
## # weights:  127
## initial  value 10659.007608 
## iter  10 value 9247.286777
## iter  20 value 9222.142116
## iter  30 value 8324.416755
## iter  40 value 8247.352341
## iter  50 value 8170.534585
## iter  60 value 7862.378072
## iter  70 value 7703.649611
## iter  80 value 7373.939389
## iter  90 value 7230.730871
## iter 100 value 7156.099129
## final  value 7156.099129 
## stopped after 100 iterations
## # weights:  175
## initial  value 10946.170284 
## iter  10 value 9255.736285
## iter  20 value 9237.456698
## iter  30 value 7349.340531
## iter  40 value 6661.022000
## iter  50 value 6549.771586
## iter  60 value 6358.193306
## iter  70 value 6099.206341
## iter  80 value 5861.954847
## iter  90 value 5577.885549
## iter 100 value 4885.492001
## final  value 4885.492001 
## stopped after 100 iterations
## # weights:  175
## initial  value 17348.511101 
## iter  10 value 14254.293286
## iter  20 value 13867.066144
## iter  30 value 13866.894387
## iter  40 value 13866.346492
## iter  40 value 13866.346418
## iter  50 value 13324.448686
## iter  60 value 12931.989302
## iter  70 value 12627.876565
## iter  80 value 12477.263992
## iter  90 value 12369.966460
## iter 100 value 12106.672708
## final  value 12106.672708 
## stopped after 100 iterations
DryBean_TDA_KDE_5.40.5_n1_NN1Fit0
## Neural Network 
## 
## 7503 samples
##   16 predictor
##    7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 5002, 5001, 5003 
## Resampling results across tuning parameters:
## 
##   size  decay  Accuracy   Kappa    
##   2     0.3    0.4163717  0.2682725
##   2     0.5    0.4028968  0.2308271
##   2     0.7    0.3845748  0.2061201
##   3     0.3    0.5143307  0.3963713
##   3     0.5    0.3658739  0.2025411
##   3     0.7    0.4497387  0.2990666
##   5     0.3    0.3770220  0.2014786
##   5     0.5    0.5835155  0.4761170
##   5     0.7    0.5518963  0.4381297
##   7     0.3    0.4723539  0.3505523
##   7     0.5    0.4602526  0.3070849
##   7     0.7    0.6145450  0.5225429
## 
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 7 and decay = 0.7.
DryBean_TDA_KDE_5.40.5_n1_NN1Fit0$resample
##    Accuracy     Kappa Resample
## 1 0.6618705 0.5844715    Fold2
## 2 0.5885646 0.4932839    Fold1
## 3 0.5932000 0.4898732    Fold3
nb_tda_kde_5.40.5_n1_nn1_fit_re<-DryBean_TDA_KDE_5.40.5_n1_NN1Fit0$resample[1]

summary(DryBean_TDA_KDE_5.40.5_n1_NN1Fit0)
## a 16-7-7 network with 175 weights
## options were - softmax modelling  decay=0.7
##   b->h1  i1->h1  i2->h1  i3->h1  i4->h1  i5->h1  i6->h1  i7->h1  i8->h1  i9->h1 
##   -0.01    0.05    1.18   -1.83   -1.82   -0.02    0.00   -0.04   -1.69    0.02 
## i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1 i16->h1 
##   -0.02   -0.03   -0.02    0.00    0.00   -0.02   -0.02 
##   b->h2  i1->h2  i2->h2  i3->h2  i4->h2  i5->h2  i6->h2  i7->h2  i8->h2  i9->h2 
##    0.00   -0.19    0.00    0.00    0.00    0.00    0.00   -0.19    0.00    0.00 
## i10->h2 i11->h2 i12->h2 i13->h2 i14->h2 i15->h2 i16->h2 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h3  i1->h3  i2->h3  i3->h3  i4->h3  i5->h3  i6->h3  i7->h3  i8->h3  i9->h3 
##    0.00    0.06    0.00    0.00    0.00    0.00    0.00    0.06    0.00    0.00 
## i10->h3 i11->h3 i12->h3 i13->h3 i14->h3 i15->h3 i16->h3 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h4  i1->h4  i2->h4  i3->h4  i4->h4  i5->h4  i6->h4  i7->h4  i8->h4  i9->h4 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
## i10->h4 i11->h4 i12->h4 i13->h4 i14->h4 i15->h4 i16->h4 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h5  i1->h5  i2->h5  i3->h5  i4->h5  i5->h5  i6->h5  i7->h5  i8->h5  i9->h5 
##    0.00   -0.02    0.00    0.00    0.00    0.00    0.00   -0.02    0.00    0.00 
## i10->h5 i11->h5 i12->h5 i13->h5 i14->h5 i15->h5 i16->h5 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h6  i1->h6  i2->h6  i3->h6  i4->h6  i5->h6  i6->h6  i7->h6  i8->h6  i9->h6 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
## i10->h6 i11->h6 i12->h6 i13->h6 i14->h6 i15->h6 i16->h6 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h7  i1->h7  i2->h7  i3->h7  i4->h7  i5->h7  i6->h7  i7->h7  i8->h7  i9->h7 
##    0.00    0.79    0.02    0.01    0.00    0.00    0.00    0.80    0.01    0.00 
## i10->h7 i11->h7 i12->h7 i13->h7 i14->h7 i15->h7 i16->h7 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##  b->o1 h1->o1 h2->o1 h3->o1 h4->o1 h5->o1 h6->o1 h7->o1 
##   0.22   0.24   0.00   0.19   0.22  -0.03   0.16   0.18 
##  b->o2 h1->o2 h2->o2 h3->o2 h4->o2 h5->o2 h6->o2 h7->o2 
##  -0.96   6.15   0.00  -0.89  -0.96   0.08  -0.80  -0.83 
##  b->o3 h1->o3 h2->o3 h3->o3 h4->o3 h5->o3 h6->o3 h7->o3 
##   0.09   1.97   0.00   0.05   0.09   0.01   0.05   0.07 
##  b->o4 h1->o4 h2->o4 h3->o4 h4->o4 h5->o4 h6->o4 h7->o4 
##   0.24  -5.70   0.01   0.23   0.24   0.04   0.27   0.22 
##  b->o5 h1->o5 h2->o5 h3->o5 h4->o5 h5->o5 h6->o5 h7->o5 
##   0.32  -1.09   0.00   0.26   0.32   0.02   0.27   0.26 
##  b->o6 h1->o6 h2->o6 h3->o6 h4->o6 h5->o6 h6->o6 h7->o6 
##   0.09  -0.76   0.00   0.09   0.09   0.00   0.09   0.09 
##  b->o7 h1->o7 h2->o7 h3->o7 h4->o7 h5->o7 h6->o7 h7->o7 
##   0.00  -0.80   0.00   0.06   0.00  -0.10  -0.04   0.01
#vip(DryBean_TDA_KDE_5.40.5_n1_NN1Fit0,50) + ggtitle("DryBean_TDA_KDE_5.40.5_n1_NN1Fit TDA-Assited NN")

# Predict outcome using DryBean_TDA_KDE_5.40.5_n1_NN1Fit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.40.5_n1_NN1Fit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.40.5_n1_db_nn1_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
nb_tda_kde_5.40.5_n1_db_nn1_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI          324    156  329        1    67     8    2
##   DERMASON        0      0    0        0     0     0    0
##   HOROZ          72      0  160     1062   511   600  788
##   SEKER           0      0    0        0     0     0    0
##   SIRA            0      0    0        0     0     0    0
## 
## Overall Statistics
##                                           
##                Accuracy : 0.2059          
##                  95% CI : (0.1936, 0.2186)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : 1               
##                                           
##                   Kappa : 0.0799          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000     0.67280          0.0000
## Specificity                  1.00000       1.00000     0.84461          1.0000
## Pos Pred Value                   NaN           NaN     0.37091             NaN
## Neg Pred Value               0.90294       0.96176     0.94989          0.7395
## Prevalence                   0.09706       0.03824     0.11985          0.2605
## Detection Rate               0.00000       0.00000     0.08064          0.0000
## Detection Prevalence         0.00000       0.00000     0.21740          0.0000
## Balanced Accuracy            0.50000       0.50000     0.75871          0.5000
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.8841        0.000      0.0000
## Specificity                0.2342        1.000      1.0000
## Pos Pred Value             0.1600          NaN         NaN
## Neg Pred Value             0.9245        0.851      0.8064
## Prevalence                 0.1417        0.149      0.1936
## Detection Rate             0.1252        0.000      0.0000
## Detection Prevalence       0.7826        0.000      0.0000
## Balanced Accuracy          0.5591        0.500      0.5000
nb_tda_kde_5.40.5_n1_db_nn1_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI          324    156  329        1    67     8    2
##   DERMASON        0      0    0        0     0     0    0
##   HOROZ          72      0  160     1062   511   600  788
##   SEKER           0      0    0        0     0     0    0
##   SIRA            0      0    0        0     0     0    0
## 
## Overall Statistics
##                                           
##                Accuracy : 0.2059          
##                  95% CI : (0.1936, 0.2186)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : 1               
##                                           
##                   Kappa : 0.0799          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000     0.67280          0.0000
## Specificity                  1.00000       1.00000     0.84461          1.0000
## Pos Pred Value                   NaN           NaN     0.37091             NaN
## Neg Pred Value               0.90294       0.96176     0.94989          0.7395
## Prevalence                   0.09706       0.03824     0.11985          0.2605
## Detection Rate               0.00000       0.00000     0.08064          0.0000
## Detection Prevalence         0.00000       0.00000     0.21740          0.0000
## Balanced Accuracy            0.50000       0.50000     0.75871          0.5000
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.8841        0.000      0.0000
## Specificity                0.2342        1.000      1.0000
## Pos Pred Value             0.1600          NaN         NaN
## Neg Pred Value             0.9245        0.851      0.8064
## Prevalence                 0.1417        0.149      0.1936
## Detection Rate             0.1252        0.000      0.0000
## Detection Prevalence       0.7826        0.000      0.0000
## Balanced Accuracy          0.5591        0.500      0.5000
nb_tda_kde_5.40.5_n1_db_nn1_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##     0.20588235     0.07989801     0.19356672     0.21862056     0.26053922 
## AccuracyPValue  McnemarPValue 
##     1.00000000            NaN
nb_tda_kde_5.40.5_n1_db_nn1_cf0_ov_acc<-nb_tda_kde_5.40.5_n1_db_nn1_cf0$overall[1]
nb_tda_kde_5.40.5_n1_db_nn1_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.0000000   1.0000000            NaN      0.9029412        NA
## Class: BOMBAY     0.0000000   1.0000000            NaN      0.9617647        NA
## Class: CALI       0.6728016   0.8446115      0.3709132      0.9498904 0.3709132
## Class: DERMASON   0.0000000   1.0000000            NaN      0.7394608        NA
## Class: HOROZ      0.8840830   0.2341519      0.1600376      0.9244645 0.1600376
## Class: SEKER      0.0000000   1.0000000            NaN      0.8509804        NA
## Class: SIRA       0.0000000   1.0000000            NaN      0.8063725        NA
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.0000000        NA 0.09705882     0.00000000
## Class: BOMBAY   0.0000000        NA 0.03823529     0.00000000
## Class: CALI     0.6728016 0.4781977 0.11985294     0.08063725
## Class: DERMASON 0.0000000        NA 0.26053922     0.00000000
## Class: HOROZ    0.8840830 0.2710156 0.14166667     0.12524510
## Class: SEKER    0.0000000        NA 0.14901961     0.00000000
## Class: SIRA     0.0000000        NA 0.19362745     0.00000000
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA             0.000000         0.5000000
## Class: BOMBAY               0.000000         0.5000000
## Class: CALI                 0.217402         0.7587066
## Class: DERMASON             0.000000         0.5000000
## Class: HOROZ                0.782598         0.5591175
## Class: SEKER                0.000000         0.5000000
## Class: SIRA                 0.000000         0.5000000
nb_tda_kde_5.40.5_n1_db_nn1_cf0_pre_rec_f1<-nb_tda_kde_5.40.5_n1_db_nn1_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_kde_5.40.5_nn1_n1_3_fold<-(db_nn1_fit_re - nb_tda_kde_5.40.5_n1_nn1_fit_re)
diff_drybean_tda_kde_5.40.5_nn1_n1_3_fold
##     Accuracy
## 1 -0.1875236
## 2  0.1508122
## 3 -0.3328915
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_nn1.n1_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n1_3_fold),-0.01,0.01)
bst_tda_kde_5.40.5_nn1.n1_3_fold
## $probLeft
## [1] 0.5
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0.25
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_nn1.n1_3_fold_odds.left<-bst_tda_kde_5.40.5_nn1.n1_3_fold$probLeft/bst_tda_kde_5.40.5_nn1.n1_3_fold$probRight
bst_tda_kde_5.40.5_nn1.n1_3_fold_odds.left
## [1] 2
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_nn1.n1_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n1_3_fold),-0.01,0.01)
bsr_tda_kde_5.40.5_nn1.n1_3_fold
## $winLeft
## [1] 0.875
## 
## $winRope
## [1] 0.0165
## 
## $winRight
## [1] 0.1085
# Bayesian Correlated Test

bct_tda_kde_5.40.5_nn1.n1_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n1_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_nn1.n1_3_fold
## $left
## [1] 0.7177491
## 
## $rope
## [1] 0.02960923
## 
## $right
## [1] 0.2526416
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.40.5_nn1_n1_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_kde_5.40.5_nn1.n1_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n1_3_fold))
#bf_tda_kde_5.40.5_nn1.n1_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n1_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n1_3_fold)
## t = -0.85981, df = 2, p-value = 0.4805
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.7397246  0.4933226
## sample estimates:
## mean of x 
## -0.123201
### Test set diff
diff_drybean_tda_kde_5.40.5_nn1.n1_test<-(db_nn1_cf_ov_acc - nb_tda_kde_5.40.5_n1_db_nn1_cf0_ov_acc)
diff_drybean_tda_kde_5.40.5_nn1.n1_test
##  Accuracy 
## 0.1720588
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_nn1.n1_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n1_test),-0.01,0.01)
bst_tda_kde_5.40.5_nn1.n1_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_nn1.n1_test_odds.left<-bst_tda_kde_5.40.5_nn1.n1_test$probLeft/bst_tda_kde_5.40.5_nn1.n1_test$probRight
bst_tda_kde_5.40.5_nn1.n1_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_nn1.n1_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n1_test),-0.01,0.01)
bsr_tda_kde_5.40.5_nn1.n1_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1578667
## 
## $winRight
## [1] 0.8421333
# Bayesian Correlated Test

bct_tda_kde_5.40.5_nn1.n1_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n1_test),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_nn1.n1_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_nn1.n1_test)))

#BayesFactor
#bf_tda_kde_5.40.5_nn1.n1_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n1_test)) #bf_tda_pca_5.40.5_nn1.n1_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n1_test))

##Node2

#Neural Network 1
DryBean_TDA_KDE_5.40.5_n2_NN1Fit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.40.5.n2.vec, 
                           Importance = T,
                     method = 'nnet', 
                     trControl = fitControl,
                     tuneGrid = nn1Grid,
                     metric='Accuracy')
## # weights:  52
## initial  value 9556.781358 
## iter  10 value 7573.789388
## iter  20 value 7573.583511
## final  value 7573.482997 
## converged
## # weights:  75
## initial  value 8339.854982 
## iter  10 value 7574.762732
## iter  20 value 7573.498252
## final  value 7573.482918 
## converged
## # weights:  121
## initial  value 9015.216627 
## iter  10 value 7573.416628
## final  value 7573.414345 
## converged
## # weights:  167
## initial  value 9940.688748 
## iter  10 value 7573.414792
## final  value 7573.414377 
## converged
## # weights:  52
## initial  value 9599.044861 
## iter  10 value 7589.933951
## iter  20 value 7573.924805
## iter  30 value 7573.669114
## final  value 7573.665999 
## converged
## # weights:  75
## initial  value 9673.522070 
## iter  10 value 7584.239061
## iter  20 value 7574.022570
## iter  30 value 7573.894395
## iter  40 value 7573.692636
## iter  50 value 7291.468579
## iter  60 value 5835.096445
## iter  70 value 5508.070449
## iter  80 value 5426.961330
## iter  90 value 5403.300342
## iter 100 value 4839.528939
## final  value 4839.528939 
## stopped after 100 iterations
## # weights:  121
## initial  value 10424.520348 
## iter  10 value 7578.547221
## iter  20 value 7573.612182
## iter  30 value 7573.552333
## final  value 7573.551668 
## converged
## # weights:  167
## initial  value 8495.576497 
## iter  10 value 7573.660551
## iter  20 value 7573.552887
## final  value 7573.551696 
## converged
## # weights:  52
## initial  value 8359.838172 
## iter  10 value 7575.852985
## iter  20 value 7574.986304
## iter  30 value 7574.179146
## iter  40 value 7574.125980
## iter  50 value 7573.872978
## iter  60 value 7573.849014
## iter  60 value 7573.849008
## iter  60 value 7573.849008
## final  value 7573.849008 
## converged
## # weights:  75
## initial  value 8057.487920 
## iter  10 value 7574.198950
## iter  20 value 7574.018651
## iter  30 value 7573.868880
## final  value 7573.848943 
## converged
## # weights:  121
## initial  value 8606.371392 
## iter  10 value 7574.279313
## iter  20 value 7497.681301
## iter  30 value 5734.564040
## iter  40 value 5330.699721
## iter  50 value 5111.615923
## iter  60 value 4481.404960
## iter  70 value 4105.984439
## iter  80 value 3882.439212
## iter  90 value 3479.626300
## iter 100 value 3122.035117
## final  value 3122.035117 
## stopped after 100 iterations
## # weights:  167
## initial  value 8135.149955 
## iter  10 value 7574.072220
## iter  20 value 7481.976886
## iter  30 value 6845.032506
## iter  40 value 6026.184716
## iter  50 value 5830.409246
## iter  60 value 5315.793911
## iter  70 value 5199.638817
## iter  80 value 5183.975322
## iter  90 value 4316.184972
## iter 100 value 3690.526958
## final  value 3690.526958 
## stopped after 100 iterations
## # weights:  52
## initial  value 8361.708636 
## iter  10 value 7577.438244
## final  value 7577.437661 
## converged
## # weights:  75
## initial  value 9523.845683 
## iter  10 value 7577.438402
## final  value 7577.437545 
## converged
## # weights:  121
## initial  value 9290.718843 
## iter  10 value 7577.437935
## final  value 7577.437371 
## converged
## # weights:  167
## initial  value 9920.635228 
## iter  10 value 7580.844837
## iter  20 value 7147.678118
## iter  30 value 5928.926139
## iter  40 value 5548.493623
## iter  50 value 5437.910917
## iter  60 value 5366.505843
## iter  70 value 5297.347841
## iter  80 value 5092.078349
## iter  90 value 4759.767661
## iter 100 value 3200.279159
## final  value 3200.279159 
## stopped after 100 iterations
## # weights:  52
## initial  value 8427.014439 
## iter  10 value 7608.469056
## iter  20 value 7578.236314
## iter  30 value 7577.853583
## iter  40 value 7577.848255
## iter  50 value 7577.631484
## final  value 7577.620556 
## converged
## # weights:  75
## initial  value 8336.287993 
## iter  10 value 7596.190269
## iter  20 value 7578.089034
## iter  30 value 7577.847612
## iter  40 value 7207.131607
## iter  50 value 5884.168128
## iter  60 value 5674.967996
## iter  70 value 5153.836845
## iter  80 value 4822.132169
## iter  90 value 4460.730048
## iter 100 value 4386.886799
## final  value 4386.886799 
## stopped after 100 iterations
## # weights:  121
## initial  value 8481.909938 
## iter  10 value 7599.324993
## iter  20 value 7577.871340
## iter  30 value 7577.510636
## iter  40 value 7339.804173
## iter  50 value 5721.229277
## iter  60 value 5504.708165
## iter  70 value 5033.741019
## iter  80 value 4904.214448
## iter  90 value 4873.066486
## iter 100 value 4653.404491
## final  value 4653.404491 
## stopped after 100 iterations
## # weights:  167
## initial  value 9954.042270 
## iter  10 value 7624.152521
## iter  20 value 7577.811892
## iter  30 value 7577.397416
## iter  40 value 7574.193965
## iter  50 value 7293.913770
## iter  60 value 6224.812074
## iter  70 value 5976.554489
## iter  80 value 5508.402360
## iter  90 value 5365.502290
## iter 100 value 4695.351057
## final  value 4695.351057 
## stopped after 100 iterations
## # weights:  52
## initial  value 11083.285365 
## iter  10 value 7595.864320
## iter  20 value 7578.092260
## iter  30 value 7562.912361
## iter  40 value 6259.383965
## iter  50 value 6034.802583
## iter  60 value 5484.733308
## iter  70 value 4889.646204
## iter  80 value 4640.773950
## iter  90 value 4514.903554
## iter 100 value 4074.408384
## final  value 4074.408384 
## stopped after 100 iterations
## # weights:  75
## initial  value 8312.643396 
## iter  10 value 7578.125050
## final  value 7578.123035 
## converged
## # weights:  121
## initial  value 10246.909165 
## iter  10 value 7578.348126
## iter  20 value 7533.840635
## iter  30 value 5259.852665
## iter  40 value 4462.787497
## iter  50 value 4179.408075
## iter  60 value 4072.341237
## iter  70 value 3972.135277
## iter  80 value 3911.356309
## iter  90 value 3860.095045
## iter 100 value 3782.379525
## final  value 3782.379525 
## stopped after 100 iterations
## # weights:  167
## initial  value 8391.634950 
## iter  10 value 7581.409121
## iter  20 value 7577.846124
## iter  30 value 7573.738275
## iter  40 value 5678.103310
## iter  50 value 5529.373631
## iter  60 value 5421.949890
## iter  70 value 4414.883964
## iter  80 value 3688.459266
## iter  90 value 3469.462657
## iter 100 value 3274.634921
## final  value 3274.634921 
## stopped after 100 iterations
## # weights:  52
## initial  value 10176.783199 
## iter  10 value 7630.882494
## iter  20 value 7579.194773
## iter  30 value 7425.501898
## iter  40 value 6462.963334
## iter  50 value 5190.476296
## iter  60 value 5068.064649
## iter  70 value 4982.291039
## iter  80 value 4875.190145
## iter  90 value 4777.189231
## iter 100 value 4392.416494
## final  value 4392.416494 
## stopped after 100 iterations
## # weights:  75
## initial  value 10486.662558 
## iter  10 value 7577.410261
## final  value 7577.326381 
## converged
## # weights:  121
## initial  value 11243.195055 
## iter  10 value 7577.704691
## iter  20 value 7478.079134
## iter  30 value 7359.653245
## iter  40 value 6399.398395
## iter  50 value 5249.442674
## iter  60 value 5061.051891
## iter  70 value 4950.410786
## iter  80 value 4933.146613
## iter  90 value 4889.404128
## iter 100 value 4825.226273
## final  value 4825.226273 
## stopped after 100 iterations
## # weights:  167
## initial  value 8444.932446 
## iter  10 value 7577.226124
## final  value 7577.217012 
## converged
## # weights:  52
## initial  value 8578.152330 
## iter  10 value 7584.314269
## iter  20 value 7577.453449
## iter  30 value 6715.796913
## iter  40 value 6447.098982
## iter  50 value 6137.718846
## iter  60 value 5602.523860
## iter  70 value 5463.967124
## iter  80 value 5325.060374
## iter  90 value 5083.644334
## iter 100 value 4914.696341
## final  value 4914.696341 
## stopped after 100 iterations
## # weights:  75
## initial  value 9726.904780 
## iter  10 value 7760.237956
## iter  20 value 7580.748892
## iter  30 value 7579.370825
## iter  40 value 7577.573198
## iter  50 value 7577.514162
## iter  50 value 7577.514153
## iter  50 value 7577.514145
## final  value 7577.514145 
## converged
## # weights:  121
## initial  value 8410.181128 
## iter  10 value 7612.242835
## iter  20 value 7493.766579
## iter  30 value 6457.768132
## iter  40 value 6193.939990
## iter  50 value 5921.416187
## iter  60 value 5680.438962
## iter  70 value 5675.708544
## iter  80 value 5673.388429
## iter  90 value 5165.482997
## iter 100 value 3908.868868
## final  value 3908.868868 
## stopped after 100 iterations
## # weights:  167
## initial  value 10466.306346 
## iter  10 value 7602.828743
## iter  20 value 7577.562813
## iter  30 value 7577.283831
## final  value 7577.280600 
## converged
## # weights:  52
## initial  value 9995.661126 
## iter  10 value 7577.708999
## iter  20 value 7577.690408
## final  value 7577.690237 
## converged
## # weights:  75
## initial  value 9085.596067 
## iter  10 value 7577.567382
## iter  20 value 7577.531134
## iter  20 value 7577.531102
## final  value 7577.531002 
## converged
## # weights:  121
## initial  value 8540.012849 
## iter  10 value 7577.691150
## iter  20 value 7577.608801
## iter  30 value 5946.207892
## iter  40 value 5491.104393
## iter  50 value 5409.434680
## iter  60 value 5235.900425
## iter  70 value 4712.605687
## iter  80 value 4221.717486
## iter  90 value 3907.546918
## iter 100 value 3727.824629
## final  value 3727.824629 
## stopped after 100 iterations
## # weights:  167
## initial  value 8394.060527 
## iter  10 value 7577.749471
## iter  20 value 7577.234884
## iter  30 value 5818.056771
## iter  40 value 5473.054691
## iter  50 value 5391.601875
## iter  60 value 5314.983351
## iter  70 value 4032.892554
## iter  80 value 3706.201068
## iter  90 value 3601.872870
## iter 100 value 2974.106494
## final  value 2974.106494 
## stopped after 100 iterations
## # weights:  167
## initial  value 15593.541906 
## iter  10 value 11692.316597
## iter  20 value 11373.261503
## iter  30 value 11365.905610
## iter  40 value 11322.255996
## iter  50 value 11053.343973
## iter  60 value 9577.943969
## iter  70 value 9261.906148
## iter  80 value 9040.193812
## iter  90 value 8913.218857
## iter 100 value 8328.652598
## final  value 8328.652598 
## stopped after 100 iterations
DryBean_TDA_KDE_5.40.5_n2_NN1Fit0
## Neural Network 
## 
## 7002 samples
##   16 predictor
##    6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 4667, 4669, 4668 
## Resampling results across tuning parameters:
## 
##   size  decay  Accuracy   Kappa    
##   2     0.3    0.4115967  0.1766426
##   2     0.5    0.3800343  0.1229214
##   2     0.7    0.3827861  0.1281538
##   3     0.3    0.2934876  0.0000000
##   3     0.5    0.5309946  0.3497537
##   3     0.7    0.2934876  0.0000000
##   5     0.3    0.3821766  0.1258834
##   5     0.5    0.5276008  0.3553392
##   5     0.7    0.7420541  0.6591329
##   7     0.3    0.4450807  0.2223132
##   7     0.5    0.3949307  0.1531822
##   7     0.7    0.7469392  0.6701730
## 
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 7 and decay = 0.7.
DryBean_TDA_KDE_5.40.5_n2_NN1Fit0$resample
##    Accuracy     Kappa Resample
## 1 0.7539649 0.6754634    Fold2
## 2 0.6856531 0.5941632    Fold1
## 3 0.8011997 0.7408924    Fold3
nb_tda_kde_5.40.5_n2_nn1_fit_re<-DryBean_TDA_KDE_5.40.5_n2_NN1Fit0$resample[1]

summary(DryBean_TDA_KDE_5.40.5_n2_NN1Fit0)
## a 16-7-6 network with 167 weights
## options were - softmax modelling  decay=0.7
##   b->h1  i1->h1  i2->h1  i3->h1  i4->h1  i5->h1  i6->h1  i7->h1  i8->h1  i9->h1 
##    0.00   -0.01    0.00    0.00    0.00    0.00    0.00    0.01    0.00    0.00 
## i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1 i16->h1 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h2  i1->h2  i2->h2  i3->h2  i4->h2  i5->h2  i6->h2  i7->h2  i8->h2  i9->h2 
##    0.00   -0.11    0.00    0.00    0.00    0.00    0.00   -0.11    0.00    0.00 
## i10->h2 i11->h2 i12->h2 i13->h2 i14->h2 i15->h2 i16->h2 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h3  i1->h3  i2->h3  i3->h3  i4->h3  i5->h3  i6->h3  i7->h3  i8->h3  i9->h3 
##    0.00    0.00   -0.05    0.04   -0.02    0.00    0.00    0.00    0.00    0.00 
## i10->h3 i11->h3 i12->h3 i13->h3 i14->h3 i15->h3 i16->h3 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h4  i1->h4  i2->h4  i3->h4  i4->h4  i5->h4  i6->h4  i7->h4  i8->h4  i9->h4 
##    0.00   -0.06    0.00    0.00    0.00    0.00    0.00   -0.06    0.00    0.00 
## i10->h4 i11->h4 i12->h4 i13->h4 i14->h4 i15->h4 i16->h4 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h5  i1->h5  i2->h5  i3->h5  i4->h5  i5->h5  i6->h5  i7->h5  i8->h5  i9->h5 
##    0.00   -0.01   -0.04    0.00   -0.03    0.00    0.00    0.01   -0.02    0.00 
## i10->h5 i11->h5 i12->h5 i13->h5 i14->h5 i15->h5 i16->h5 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h6  i1->h6  i2->h6  i3->h6  i4->h6  i5->h6  i6->h6  i7->h6  i8->h6  i9->h6 
##    0.00   -0.01    0.00    0.00    0.00    0.00    0.00   -0.01    0.00    0.00 
## i10->h6 i11->h6 i12->h6 i13->h6 i14->h6 i15->h6 i16->h6 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h7  i1->h7  i2->h7  i3->h7  i4->h7  i5->h7  i6->h7  i7->h7  i8->h7  i9->h7 
##    0.00    0.00    0.02    0.36    0.80   -0.01   -0.03   -0.01   -0.58    0.02 
## i10->h7 i11->h7 i12->h7 i13->h7 i14->h7 i15->h7 i16->h7 
##    0.00    0.00    0.01    0.00    0.00    0.02    0.00 
##  b->o1 h1->o1 h2->o1 h3->o1 h4->o1 h5->o1 h6->o1 h7->o1 
##   0.59   0.68  -0.10  -0.10   0.56   0.68  -0.10  -4.55 
##  b->o2 h1->o2 h2->o2 h3->o2 h4->o2 h5->o2 h6->o2 h7->o2 
##   0.97   0.99  -0.03  -0.09   1.28   0.99  -0.03  -5.28 
##  b->o3 h1->o3 h2->o3 h3->o3 h4->o3 h5->o3 h6->o3 h7->o3 
##  -0.56  -0.59   0.03   0.07  -0.56  -0.59   0.03   3.38 
##  b->o4 h1->o4 h2->o4 h3->o4 h4->o4 h5->o4 h6->o4 h7->o4 
##  -0.03  -0.09   0.06   0.00  -0.17  -0.09   0.06   0.92 
##  b->o5 h1->o5 h2->o5 h3->o5 h4->o5 h5->o5 h6->o5 h7->o5 
##  -1.01  -1.01   0.00   0.00  -0.89  -1.01   0.00   3.90 
##  b->o6 h1->o6 h2->o6 h3->o6 h4->o6 h5->o6 h6->o6 h7->o6 
##   0.05   0.01   0.04   0.12  -0.21   0.02   0.04   1.62
#vip(DryBean_TDA_KDE_5.40.5_n2_NN1Fit0,50) + ggtitle("DryBean_TDA_KDE_5.40.5_n2_NN1Fit TDA-Assited NN")

# Predict outcome using DryBean_TDA_KDE_5.40.5_n2_NN1Fit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.40.5_n2_NN1Fit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.40.5_n2_db_nn1_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length

## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
nb_tda_kde_5.40.5_n2_db_nn1_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI          320    156  468        0   136     0    1
##   DERMASON        1      0    3     1057    62   575  507
##   HOROZ           0      0    0        0     0     0    0
##   SEKER           0      0    0        0     0     0    0
##   SIRA           75      0   18        6   380    33  282
## 
## Overall Statistics
##                                           
##                Accuracy : 0.4429          
##                  95% CI : (0.4276, 0.4583)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.2946          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000      0.9571          0.9944
## Specificity                  1.00000       1.00000      0.8293          0.6195
## Pos Pred Value                   NaN           NaN      0.4329          0.4794
## Neg Pred Value               0.90294       0.96176      0.9930          0.9968
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.00000       0.00000      0.1147          0.2591
## Detection Prevalence         0.00000       0.00000      0.2650          0.5404
## Balanced Accuracy            0.50000       0.50000      0.8932          0.8069
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.0000        0.000     0.35696
## Specificity                1.0000        1.000     0.84438
## Pos Pred Value                NaN          NaN     0.35516
## Neg Pred Value             0.8583        0.851     0.84540
## Prevalence                 0.1417        0.149     0.19363
## Detection Rate             0.0000        0.000     0.06912
## Detection Prevalence       0.0000        0.000     0.19461
## Balanced Accuracy          0.5000        0.500     0.60067
nb_tda_kde_5.40.5_n2_db_nn1_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI          320    156  468        0   136     0    1
##   DERMASON        1      0    3     1057    62   575  507
##   HOROZ           0      0    0        0     0     0    0
##   SEKER           0      0    0        0     0     0    0
##   SIRA           75      0   18        6   380    33  282
## 
## Overall Statistics
##                                           
##                Accuracy : 0.4429          
##                  95% CI : (0.4276, 0.4583)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.2946          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000      0.9571          0.9944
## Specificity                  1.00000       1.00000      0.8293          0.6195
## Pos Pred Value                   NaN           NaN      0.4329          0.4794
## Neg Pred Value               0.90294       0.96176      0.9930          0.9968
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.00000       0.00000      0.1147          0.2591
## Detection Prevalence         0.00000       0.00000      0.2650          0.5404
## Balanced Accuracy            0.50000       0.50000      0.8932          0.8069
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.0000        0.000     0.35696
## Specificity                1.0000        1.000     0.84438
## Pos Pred Value                NaN          NaN     0.35516
## Neg Pred Value             0.8583        0.851     0.84540
## Prevalence                 0.1417        0.149     0.19363
## Detection Rate             0.0000        0.000     0.06912
## Detection Prevalence       0.0000        0.000     0.19461
## Balanced Accuracy          0.5000        0.500     0.60067
nb_tda_kde_5.40.5_n2_db_nn1_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   4.428922e-01   2.945835e-01   4.275733e-01   4.582929e-01   2.605392e-01 
## AccuracyPValue  McnemarPValue 
##  3.159817e-139            NaN
nb_tda_kde_5.40.5_n2_db_nn1_cf0_ov_acc<-nb_tda_kde_5.40.5_n2_db_nn1_cf0$overall[1]
nb_tda_kde_5.40.5_n2_db_nn1_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.0000000   1.0000000            NaN      0.9029412        NA
## Class: BOMBAY     0.0000000   1.0000000            NaN      0.9617647        NA
## Class: CALI       0.9570552   0.8292955      0.4329325      0.9929977 0.4329325
## Class: DERMASON   0.9943556   0.6194896      0.4793651      0.9968000 0.4793651
## Class: HOROZ      0.0000000   1.0000000            NaN      0.8583333        NA
## Class: SEKER      0.0000000   1.0000000            NaN      0.8509804        NA
## Class: SIRA       0.3569620   0.8443769      0.3551637      0.8454047 0.3551637
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.0000000        NA 0.09705882     0.00000000
## Class: BOMBAY   0.0000000        NA 0.03823529     0.00000000
## Class: CALI     0.9570552 0.5961783 0.11985294     0.11470588
## Class: DERMASON 0.9943556 0.6468788 0.26053922     0.25906863
## Class: HOROZ    0.0000000        NA 0.14166667     0.00000000
## Class: SEKER    0.0000000        NA 0.14901961     0.00000000
## Class: SIRA     0.3569620 0.3560606 0.19362745     0.06911765
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA            0.0000000         0.5000000
## Class: BOMBAY              0.0000000         0.5000000
## Class: CALI                0.2649510         0.8931753
## Class: DERMASON            0.5404412         0.8069226
## Class: HOROZ               0.0000000         0.5000000
## Class: SEKER               0.0000000         0.5000000
## Class: SIRA                0.1946078         0.6006695
nb_tda_kde_5.40.5_n2_db_nn1_cf0_pre_rec_f1<-nb_tda_kde_5.40.5_n2_db_nn1_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_kde_5.40.5_nn1_n2_3_fold<-(db_nn1_fit_re - nb_tda_kde_5.40.5_n2_nn1_fit_re)
diff_drybean_tda_kde_5.40.5_nn1_n2_3_fold
##      Accuracy
## 1 -0.27961798
## 2  0.05372367
## 3 -0.54089119
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_nn1.n2_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n2_3_fold),-0.01,0.01)
bst_tda_kde_5.40.5_nn1.n2_3_fold
## $probLeft
## [1] 0.5
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0.25
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_nn1.n2_3_fold_odds.left<-bst_tda_kde_5.40.5_nn1.n2_3_fold$probLeft/bst_tda_kde_5.40.5_nn1.n2_3_fold$probRight
bst_tda_kde_5.40.5_nn1.n2_3_fold_odds.left
## [1] 2
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_nn1.n2_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n2_3_fold),-0.01,0.01)
bsr_tda_kde_5.40.5_nn1.n2_3_fold
## $winLeft
## [1] 0.8762667
## 
## $winRope
## [1] 0.0156
## 
## $winRight
## [1] 0.1081333
# Bayesian Correlated Test

bct_tda_kde_5.40.5_nn1.n2_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n2_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_nn1.n2_3_fold
## $left
## [1] 0.8290468
## 
## $rope
## [1] 0.01441263
## 
## $right
## [1] 0.1565405
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.40.5_nn1_n2_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_kde_5.40.5_nn1.n2_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n2_3_fold))
#bf_tda_kde_5.40.5_nn1.n2_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n2_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n2_3_fold)
## t = -1.4854, df = 2, p-value = 0.2757
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.9959538  0.4847634
## sample estimates:
##  mean of x 
## -0.2555952
### Test set diff
diff_drybean_tda_kde_5.40.5_nn1.n2_test<-(db_nn1_cf_ov_acc - nb_tda_kde_5.40.5_n2_db_nn1_cf0_ov_acc)
diff_drybean_tda_kde_5.40.5_nn1.n2_test
##    Accuracy 
## -0.06495098
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_nn1.n2_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n2_test),-0.01,0.01)
bst_tda_kde_5.40.5_nn1.n2_test
## $probLeft
## [1] 0.5
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_nn1.n2_test_odds.left<-bst_tda_kde_5.40.5_nn1.n2_test$probLeft/bst_tda_kde_5.40.5_nn1.n2_test$probRight
bst_tda_kde_5.40.5_nn1.n2_test_odds.left
## [1] Inf
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_nn1.n2_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n2_test),-0.01,0.01)
bsr_tda_kde_5.40.5_nn1.n2_test
## $winLeft
## [1] 0.8358333
## 
## $winRope
## [1] 0.1641667
## 
## $winRight
## [1] 0
# Bayesian Correlated Test

bct_tda_kde_5.40.5_nn1.n2_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n2_test),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_nn1.n2_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_nn1.n2_test)))

#BayesFactor
#bf_tda_kde_5.40.5_nn1.n2_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n2_test)) #bf_tda_pca_5.40.5_nn1.n2_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n2_test))

##Node3

#Neural Network 1
DryBean_TDA_KDE_5.40.5_n3_NN1Fit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.40.5.n3.vec, 
                           Importance = T,
                     method = 'nnet', 
                     trControl = fitControl,
                     tuneGrid = nn1Grid,
                     metric='Accuracy')
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'CALI' is empty
## # weights:  49
## initial  value 4860.083526 
## iter  10 value 2662.370285
## iter  20 value 2646.981310
## iter  30 value 2553.393035
## iter  40 value 2171.341259
## iter  50 value 1654.661006
## iter  60 value 1373.079004
## iter  70 value 983.182679
## iter  80 value 828.941189
## iter  90 value 773.759723
## iter 100 value 715.332043
## final  value 715.332043 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'CALI' is empty
## # weights:  71
## initial  value 5566.629028 
## iter  10 value 2671.774833
## iter  20 value 2333.936176
## iter  30 value 2168.438968
## iter  40 value 1848.834236
## iter  50 value 1812.944296
## iter  60 value 1754.449088
## iter  70 value 1726.571688
## iter  80 value 1701.484497
## iter  90 value 1641.114385
## iter 100 value 1593.530120
## final  value 1593.530120 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'CALI' is empty
## # weights:  115
## initial  value 3924.437445 
## iter  10 value 2676.601900
## iter  20 value 2270.019036
## iter  30 value 1973.606533
## iter  40 value 1830.709047
## iter  50 value 1767.839509
## iter  60 value 1709.045642
## iter  70 value 1646.970374
## iter  80 value 1588.122440
## iter  90 value 1259.911115
## iter 100 value 836.697822
## final  value 836.697822 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'CALI' is empty
## # weights:  159
## initial  value 4418.096181 
## iter  10 value 2672.675221
## iter  20 value 2661.727469
## iter  30 value 2600.137957
## iter  40 value 2122.426265
## iter  50 value 1874.699981
## iter  60 value 1812.317273
## iter  70 value 1784.310124
## iter  80 value 1204.275582
## iter  90 value 969.069646
## iter 100 value 899.659809
## final  value 899.659809 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'CALI' is empty
## # weights:  49
## initial  value 3459.884174 
## iter  10 value 2692.856273
## iter  20 value 2680.254559
## iter  30 value 2630.774143
## iter  40 value 2352.609223
## iter  50 value 2132.769297
## iter  60 value 1997.092092
## iter  70 value 1884.275216
## iter  80 value 1734.788180
## iter  90 value 1644.710903
## iter 100 value 1515.298198
## final  value 1515.298198 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'CALI' is empty
## # weights:  71
## initial  value 5143.895521 
## iter  10 value 2708.004530
## iter  20 value 2663.969202
## iter  30 value 2662.388520
## iter  40 value 2662.285372
## iter  50 value 2662.240233
## final  value 2662.239712 
## converged
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'CALI' is empty
## # weights:  115
## initial  value 4631.758686 
## iter  10 value 2690.652281
## iter  20 value 2667.197207
## iter  30 value 2661.860404
## iter  40 value 2661.729846
## iter  50 value 2661.705121
## iter  60 value 2661.396108
## iter  70 value 2661.340199
## final  value 2661.337060 
## converged
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'CALI' is empty
## # weights:  159
## initial  value 5175.127650 
## iter  10 value 2725.649006
## iter  20 value 2704.259997
## iter  30 value 2663.812254
## iter  40 value 2663.749017
## iter  50 value 2661.699197
## iter  60 value 2500.486675
## iter  70 value 2422.211548
## iter  80 value 1949.665658
## iter  90 value 1438.945730
## iter 100 value 1083.057087
## final  value 1083.057087 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'CALI' is empty
## # weights:  49
## initial  value 4422.264837 
## iter  10 value 2670.641203
## iter  20 value 2666.791603
## iter  30 value 2665.772003
## iter  40 value 2447.051586
## iter  50 value 1994.743620
## iter  60 value 1841.340565
## iter  70 value 1706.759542
## iter  80 value 1591.341005
## iter  90 value 1571.937891
## iter 100 value 1528.316431
## final  value 1528.316431 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'CALI' is empty
## # weights:  71
## initial  value 4909.756460 
## iter  10 value 2679.556808
## iter  20 value 2666.495097
## iter  30 value 2664.004078
## iter  40 value 2014.222064
## iter  50 value 1397.648698
## iter  60 value 1060.330593
## iter  70 value 916.051544
## iter  80 value 845.791320
## iter  90 value 808.176772
## iter 100 value 770.344968
## final  value 770.344968 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'CALI' is empty
## # weights:  115
## initial  value 4228.711205 
## iter  10 value 2714.032989
## iter  20 value 2452.709840
## iter  30 value 2341.762144
## iter  40 value 1919.096672
## iter  50 value 1202.783045
## iter  60 value 1092.345047
## iter  70 value 1011.541519
## iter  80 value 940.017673
## iter  90 value 886.632003
## iter 100 value 836.237297
## final  value 836.237297 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'CALI' is empty
## # weights:  159
## initial  value 5289.003646 
## iter  10 value 3024.329195
## iter  20 value 2669.851256
## iter  30 value 2664.735012
## iter  40 value 2664.587400
## iter  50 value 2661.838751
## iter  60 value 2661.701052
## final  value 2661.699646 
## converged
## # weights:  52
## initial  value 5292.149080 
## iter  10 value 2672.651214
## iter  20 value 2669.562134
## iter  30 value 2161.298289
## iter  40 value 1874.907754
## iter  50 value 1786.414203
## iter  60 value 1108.786195
## iter  70 value 905.329376
## iter  80 value 787.330660
## iter  90 value 751.542397
## iter 100 value 746.481101
## final  value 746.481101 
## stopped after 100 iterations
## # weights:  75
## initial  value 6052.532786 
## iter  10 value 2672.639926
## iter  20 value 2671.975314
## iter  30 value 2671.728129
## iter  40 value 2671.651252
## iter  50 value 2671.639770
## final  value 2671.639719 
## converged
## # weights:  121
## initial  value 5265.516094 
## iter  10 value 2788.276939
## iter  20 value 2673.618218
## iter  30 value 2670.835625
## iter  40 value 2670.787301
## iter  50 value 2670.682789
## final  value 2670.657038 
## converged
## # weights:  167
## initial  value 3933.521909 
## iter  10 value 2732.508122
## iter  20 value 2673.363722
## iter  30 value 2671.649914
## iter  40 value 2393.790679
## iter  50 value 1931.714843
## iter  60 value 1913.565507
## iter  70 value 1744.782498
## iter  80 value 1590.826412
## iter  90 value 1296.129237
## iter 100 value 1169.970220
## final  value 1169.970220 
## stopped after 100 iterations
## # weights:  52
## initial  value 4581.715798 
## iter  10 value 2722.609544
## iter  20 value 2707.721525
## iter  30 value 2119.082892
## iter  40 value 1924.180024
## iter  50 value 1262.142884
## iter  60 value 1098.478525
## iter  70 value 977.627442
## iter  80 value 929.059682
## iter  90 value 899.321114
## iter 100 value 864.563368
## final  value 864.563368 
## stopped after 100 iterations
## # weights:  75
## initial  value 4094.444712 
## iter  10 value 2687.830155
## iter  20 value 2678.332794
## iter  30 value 2676.006315
## iter  40 value 2631.181632
## iter  50 value 1657.628662
## iter  60 value 1129.247768
## iter  70 value 1082.282744
## iter  80 value 1046.384148
## iter  90 value 1018.174293
## iter 100 value 996.921238
## final  value 996.921238 
## stopped after 100 iterations
## # weights:  121
## initial  value 4494.716065 
## iter  10 value 2684.285951
## iter  20 value 2680.217638
## iter  30 value 2672.897060
## iter  40 value 2671.964758
## final  value 2671.963992 
## converged
## # weights:  167
## initial  value 4312.002814 
## iter  10 value 2716.606135
## iter  20 value 2685.844158
## iter  30 value 2674.662964
## iter  40 value 2673.434155
## iter  50 value 2392.663687
## iter  60 value 2286.868430
## iter  70 value 1997.478800
## iter  80 value 1225.008711
## iter  90 value 1183.790968
## iter 100 value 1038.584733
## final  value 1038.584733 
## stopped after 100 iterations
## # weights:  52
## initial  value 4331.227013 
## iter  10 value 2783.631538
## iter  20 value 2688.828237
## iter  30 value 2611.731546
## iter  40 value 2080.527621
## iter  50 value 1883.791458
## iter  60 value 1753.480636
## iter  70 value 1646.568457
## iter  80 value 1618.392925
## iter  90 value 1521.518031
## iter 100 value 1185.695669
## final  value 1185.695669 
## stopped after 100 iterations
## # weights:  75
## initial  value 5257.777079 
## iter  10 value 2721.333065
## iter  20 value 2682.984796
## iter  30 value 2681.213752
## iter  40 value 2453.987657
## iter  50 value 2081.678587
## iter  60 value 2021.741279
## iter  70 value 1788.215351
## iter  80 value 1376.896443
## iter  90 value 1093.633788
## iter 100 value 991.367262
## final  value 991.367262 
## stopped after 100 iterations
## # weights:  121
## initial  value 4448.999239 
## iter  10 value 2953.609400
## iter  20 value 2950.767477
## iter  30 value 2414.200864
## iter  40 value 1962.144502
## iter  50 value 1897.686092
## iter  60 value 1847.130326
## iter  70 value 1815.562811
## iter  80 value 1774.403556
## iter  90 value 1693.073602
## iter 100 value 1660.643773
## final  value 1660.643773 
## stopped after 100 iterations
## # weights:  167
## initial  value 6029.557264 
## iter  10 value 2747.793426
## iter  20 value 2728.596526
## iter  30 value 2673.942303
## iter  40 value 2673.417122
## iter  50 value 2673.403389
## iter  60 value 2673.259516
## iter  70 value 2672.517439
## iter  80 value 2662.359377
## iter  90 value 2404.283646
## iter 100 value 2301.457303
## final  value 2301.457303 
## stopped after 100 iterations
## # weights:  52
## initial  value 3681.508776 
## iter  10 value 2688.841368
## iter  20 value 2673.800333
## iter  30 value 2358.952942
## iter  40 value 2074.903798
## iter  50 value 1737.553065
## iter  60 value 1066.038900
## iter  70 value 948.115764
## iter  80 value 921.780358
## iter  90 value 896.133093
## iter 100 value 874.841089
## final  value 874.841089 
## stopped after 100 iterations
## # weights:  75
## initial  value 5347.761913 
## iter  10 value 2675.500791
## iter  20 value 2675.291060
## iter  30 value 2675.107338
## iter  40 value 2671.290704
## iter  50 value 2278.097746
## iter  60 value 2267.716638
## iter  70 value 1888.922748
## iter  80 value 1202.150195
## iter  90 value 971.299784
## iter 100 value 905.507516
## final  value 905.507516 
## stopped after 100 iterations
## # weights:  121
## initial  value 3494.855233 
## iter  10 value 2681.774624
## iter  20 value 2672.651097
## iter  30 value 2672.267984
## iter  40 value 2672.235851
## iter  50 value 2670.428349
## iter  60 value 2589.959413
## iter  70 value 2236.191882
## iter  80 value 1775.284689
## iter  90 value 1702.854049
## iter 100 value 1692.755170
## final  value 1692.755170 
## stopped after 100 iterations
## # weights:  167
## initial  value 3110.752642 
## iter  10 value 2691.315406
## iter  20 value 2673.333289
## iter  30 value 2671.806326
## iter  40 value 2632.725439
## iter  50 value 2003.756569
## iter  60 value 1973.311680
## iter  70 value 1880.429061
## iter  80 value 1863.867984
## iter  90 value 1839.308163
## iter 100 value 1456.240013
## final  value 1456.240013 
## stopped after 100 iterations
## # weights:  52
## initial  value 5437.471091 
## iter  10 value 2699.297896
## iter  20 value 2678.365233
## iter  30 value 2677.666630
## iter  40 value 2675.105637
## iter  50 value 2673.567011
## iter  60 value 2477.467261
## iter  70 value 2006.836843
## iter  80 value 1922.314039
## iter  90 value 1293.849797
## iter 100 value 1095.751316
## final  value 1095.751316 
## stopped after 100 iterations
## # weights:  75
## initial  value 4684.809639 
## iter  10 value 2713.019075
## iter  20 value 2674.875230
## iter  30 value 2674.726053
## iter  40 value 2673.202569
## iter  50 value 2664.552844
## iter  60 value 2643.372687
## iter  70 value 2507.695340
## iter  80 value 1971.259407
## iter  90 value 1773.507394
## iter 100 value 1697.310546
## final  value 1697.310546 
## stopped after 100 iterations
## # weights:  121
## initial  value 4023.917786 
## iter  10 value 2720.060374
## iter  20 value 2680.284538
## iter  30 value 2677.552229
## iter  40 value 2675.959324
## iter  50 value 2674.173805
## iter  60 value 2440.456422
## iter  70 value 2024.789575
## iter  80 value 1869.863120
## iter  90 value 1264.892859
## iter 100 value 1021.566591
## final  value 1021.566591 
## stopped after 100 iterations
## # weights:  167
## initial  value 3470.156061 
## iter  10 value 2679.755426
## iter  20 value 2550.248956
## iter  30 value 2414.332325
## iter  40 value 1868.276289
## iter  50 value 1737.995592
## iter  60 value 1524.866210
## iter  70 value 1405.930951
## iter  80 value 1139.785097
## iter  90 value 1024.277986
## iter 100 value 916.465824
## final  value 916.465824 
## stopped after 100 iterations
## # weights:  52
## initial  value 3806.255392 
## iter  10 value 2703.178859
## iter  20 value 2681.187538
## iter  30 value 2391.905975
## iter  40 value 2012.793390
## iter  50 value 1815.718459
## iter  60 value 1690.857113
## iter  70 value 1592.242371
## iter  80 value 1461.384688
## iter  90 value 1435.275903
## iter 100 value 1434.270742
## final  value 1434.270742 
## stopped after 100 iterations
## # weights:  75
## initial  value 4540.050895 
## iter  10 value 2708.105852
## iter  20 value 2679.325157
## iter  30 value 2584.645361
## iter  40 value 2296.921682
## iter  50 value 2014.381714
## iter  60 value 1787.118525
## iter  70 value 1765.321518
## iter  80 value 1724.691396
## iter  90 value 1613.302653
## iter 100 value 1571.908564
## final  value 1571.908564 
## stopped after 100 iterations
## # weights:  121
## initial  value 5344.189921 
## iter  10 value 2696.748045
## iter  20 value 2686.942794
## iter  30 value 2681.310235
## iter  40 value 2680.771843
## iter  50 value 2159.839737
## iter  60 value 1941.954879
## iter  70 value 1873.485028
## iter  80 value 1836.093447
## iter  90 value 1797.989539
## iter 100 value 1469.672000
## final  value 1469.672000 
## stopped after 100 iterations
## # weights:  167
## initial  value 6717.805653 
## iter  10 value 2689.655475
## iter  20 value 2673.684833
## iter  30 value 2672.570845
## iter  40 value 2672.028067
## iter  50 value 2671.469501
## iter  60 value 2657.388095
## iter  70 value 2632.522790
## iter  80 value 2610.250044
## iter  90 value 2592.187673
## iter 100 value 2377.908967
## final  value 2377.908967 
## stopped after 100 iterations
## # weights:  75
## initial  value 6287.549374 
## iter  10 value 4129.454831
## iter  20 value 4010.462816
## iter  30 value 3670.161727
## iter  40 value 2943.186051
## iter  50 value 2900.473625
## iter  60 value 2729.296774
## iter  70 value 2664.501729
## iter  80 value 2580.886373
## iter  90 value 2179.292609
## iter 100 value 1583.841830
## final  value 1583.841830 
## stopped after 100 iterations
DryBean_TDA_KDE_5.40.5_n3_NN1Fit0
## Neural Network 
## 
## 3511 samples
##   16 predictor
##    6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 2340, 2341, 2341 
## Resampling results across tuning parameters:
## 
##   size  decay  Accuracy   Kappa    
##   2     0.3    0.6028468  0.5300243
##   2     0.5    0.6407050  0.5656450
##   2     0.7    0.5675141  0.4859431
##   3     0.3    0.4233591  0.2292670
##   3     0.5    0.6685902  0.5440594
##   3     0.7    0.5213651  0.4028616
##   5     0.3    0.3518506  0.1151301
##   5     0.5    0.5087611  0.2760156
##   5     0.7    0.5393145  0.4348135
##   7     0.3    0.5623866  0.4629263
##   7     0.5    0.5977157  0.5186298
##   7     0.7    0.4460830  0.1830201
## 
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 3 and decay = 0.5.
DryBean_TDA_KDE_5.40.5_n3_NN1Fit0$resample
##    Accuracy     Kappa Resample
## 1 0.8623932 0.7917841    Fold3
## 2 0.8948718 0.8403942    Fold2
## 3 0.2485056 0.0000000    Fold1
nb_tda_kde_5.40.5_n3_nn1_fit_re<-DryBean_TDA_KDE_5.40.5_n3_NN1Fit0$resample[1]

summary(DryBean_TDA_KDE_5.40.5_n3_NN1Fit0)
## a 16-3-6 network with 75 weights
## options were - softmax modelling  decay=0.5
##   b->h1  i1->h1  i2->h1  i3->h1  i4->h1  i5->h1  i6->h1  i7->h1  i8->h1  i9->h1 
##   -0.01    0.00   -0.02    0.48    0.51    0.42    0.12    0.00   -0.90    0.18 
## i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1 i16->h1 
##   -0.01   -0.12   -0.15    0.00    0.00   -0.24   -0.02 
##   b->h2  i1->h2  i2->h2  i3->h2  i4->h2  i5->h2  i6->h2  i7->h2  i8->h2  i9->h2 
##    0.06   -0.01   -0.06   -0.64   -0.80    0.11   -0.02    0.01    1.68   -0.11 
## i10->h2 i11->h2 i12->h2 i13->h2 i14->h2 i15->h2 i16->h2 
##    0.06    0.11    0.08    0.00    0.00    0.11    0.10 
##   b->h3  i1->h3  i2->h3  i3->h3  i4->h3  i5->h3  i6->h3  i7->h3  i8->h3  i9->h3 
##   -0.02    0.00    0.21    0.76    1.87   -0.04   -0.18    0.01   -4.12    0.10 
## i10->h3 i11->h3 i12->h3 i13->h3 i14->h3 i15->h3 i16->h3 
##   -0.02   -0.04    0.02    0.00    0.00    0.06   -0.04 
##  b->o1 h1->o1 h2->o1 h3->o1 
##  -3.37  -3.41   3.22   0.61 
##  b->o2 h1->o2 h2->o2 h3->o2 
##  -1.26  -0.68   0.61  -0.02 
##  b->o3 h1->o3 h2->o3 h3->o3 
##   1.45   3.63  -1.78  -4.75 
##  b->o4 h1->o4 h2->o4 h3->o4 
##  -1.35   1.64  -0.53   0.24 
##  b->o5 h1->o5 h2->o5 h3->o5 
##   4.66  -4.67   0.38   2.12 
##  b->o6 h1->o6 h2->o6 h3->o6 
##  -0.13   3.49  -1.89   1.81
#vip(DryBean_TDA_KDE_5.40.5_n3_NN1Fit0,50) + ggtitle("DryBean_TDA_KDE_5.40.5_n3_NN1Fit TDA-Assited NN")

# Predict outcome using DryBean_TDA_KDE_5.40.5_n3_NN1Fit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.40.5_n3_NN1Fit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.40.5_n3_db_nn1_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length

## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
nb_tda_kde_5.40.5_n3_db_nn1_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     0    0
##   DERMASON        0      0    0     1011    24    20  111
##   HOROZ           0      0    0        0     0     0    0
##   SEKER          71    136    7        6     0   558    9
##   SIRA          325     20  482       46   554    30  670
## 
## Overall Statistics
##                                           
##                Accuracy : 0.5488          
##                  95% CI : (0.5334, 0.5641)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.433           
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000      0.0000          0.9511
## Specificity                  1.00000       1.00000      1.0000          0.9486
## Pos Pred Value                   NaN           NaN         NaN          0.8671
## Neg Pred Value               0.90294       0.96176      0.8801          0.9822
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.00000       0.00000      0.0000          0.2478
## Detection Prevalence         0.00000       0.00000      0.0000          0.2858
## Balanced Accuracy            0.50000       0.50000      0.5000          0.9499
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.0000       0.9178      0.8481
## Specificity                1.0000       0.9340      0.5571
## Pos Pred Value                NaN       0.7090      0.3150
## Neg Pred Value             0.8583       0.9848      0.9386
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.0000       0.1368      0.1642
## Detection Prevalence       0.0000       0.1929      0.5213
## Balanced Accuracy          0.5000       0.9259      0.7026
nb_tda_kde_5.40.5_n3_db_nn1_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     0    0
##   DERMASON        0      0    0     1011    24    20  111
##   HOROZ           0      0    0        0     0     0    0
##   SEKER          71    136    7        6     0   558    9
##   SIRA          325     20  482       46   554    30  670
## 
## Overall Statistics
##                                           
##                Accuracy : 0.5488          
##                  95% CI : (0.5334, 0.5641)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.433           
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000      0.0000          0.9511
## Specificity                  1.00000       1.00000      1.0000          0.9486
## Pos Pred Value                   NaN           NaN         NaN          0.8671
## Neg Pred Value               0.90294       0.96176      0.8801          0.9822
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.00000       0.00000      0.0000          0.2478
## Detection Prevalence         0.00000       0.00000      0.0000          0.2858
## Balanced Accuracy            0.50000       0.50000      0.5000          0.9499
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.0000       0.9178      0.8481
## Specificity                1.0000       0.9340      0.5571
## Pos Pred Value                NaN       0.7090      0.3150
## Neg Pred Value             0.8583       0.9848      0.9386
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.0000       0.1368      0.1642
## Detection Prevalence       0.0000       0.1929      0.5213
## Balanced Accuracy          0.5000       0.9259      0.7026
nb_tda_kde_5.40.5_n3_db_nn1_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.5487745      0.4330303      0.5333526      0.5641266      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
nb_tda_kde_5.40.5_n3_db_nn1_cf0_ov_acc<-nb_tda_kde_5.40.5_n3_db_nn1_cf0$overall[1]
nb_tda_kde_5.40.5_n3_db_nn1_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.0000000   1.0000000            NaN      0.9029412        NA
## Class: BOMBAY     0.0000000   1.0000000            NaN      0.9617647        NA
## Class: CALI       0.0000000   1.0000000            NaN      0.8801471        NA
## Class: DERMASON   0.9510818   0.9486245      0.8670669      0.9821551 0.8670669
## Class: HOROZ      0.0000000   1.0000000            NaN      0.8583333        NA
## Class: SEKER      0.9177632   0.9340438      0.7090216      0.9848163 0.7090216
## Class: SIRA       0.8481013   0.5571429      0.3149976      0.9385561 0.3149976
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.0000000        NA 0.09705882      0.0000000
## Class: BOMBAY   0.0000000        NA 0.03823529      0.0000000
## Class: CALI     0.0000000        NA 0.11985294      0.0000000
## Class: DERMASON 0.9510818 0.9071332 0.26053922      0.2477941
## Class: HOROZ    0.0000000        NA 0.14166667      0.0000000
## Class: SEKER    0.9177632 0.8000000 0.14901961      0.1367647
## Class: SIRA     0.8481013 0.4593761 0.19362745      0.1642157
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA            0.0000000         0.5000000
## Class: BOMBAY              0.0000000         0.5000000
## Class: CALI                0.0000000         0.5000000
## Class: DERMASON            0.2857843         0.9498532
## Class: HOROZ               0.0000000         0.5000000
## Class: SEKER               0.1928922         0.9259035
## Class: SIRA                0.5213235         0.7026221
nb_tda_kde_5.40.5_n3_db_nn1_cf0_pre_rec_f1<-nb_tda_kde_5.40.5_n3_db_nn1_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_kde_5.40.5_nn1_n3_3_fold<-(db_nn1_fit_re - nb_tda_kde_5.40.5_n3_nn1_fit_re)
diff_drybean_tda_kde_5.40.5_nn1_n3_3_fold
##      Accuracy
## 1 -0.38804629
## 2 -0.15549502
## 3  0.01180292
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_nn1.n3_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n3_3_fold),-0.01,0.01)
bst_tda_kde_5.40.5_nn1.n3_3_fold
## $probLeft
## [1] 0.5
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0.25
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_nn1.n3_3_fold_odds.left<-bst_tda_kde_5.40.5_nn1.n3_3_fold$probLeft/bst_tda_kde_5.40.5_nn1.n3_3_fold$probRight
bst_tda_kde_5.40.5_nn1.n3_3_fold_odds.left
## [1] 2
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_nn1.n3_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n3_3_fold),-0.01,0.01)
bsr_tda_kde_5.40.5_nn1.n3_3_fold
## $winLeft
## [1] 0.8839
## 
## $winRope
## [1] 0.05266667
## 
## $winRight
## [1] 0.06343333
# Bayesian Correlated Test

bct_tda_kde_5.40.5_nn1.n3_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n3_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_nn1.n3_3_fold
## $left
## [1] 0.8310269
## 
## $rope
## [1] 0.02056886
## 
## $right
## [1] 0.1484043
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.40.5_nn1_n3_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_kde_5.40.5_nn1.n3_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n3_3_fold))
#bf_tda_kde_5.40.5_nn1.n3_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n3_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n3_3_fold)
## t = -1.5288, df = 2, p-value = 0.2659
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.6760860  0.3215937
## sample estimates:
##  mean of x 
## -0.1772461
### Test set diff
diff_drybean_tda_kde_5.40.5_nn1.n3_test<-(db_nn1_cf_ov_acc - nb_tda_kde_5.40.5_n3_db_nn1_cf0_ov_acc)
diff_drybean_tda_kde_5.40.5_nn1.n3_test
##   Accuracy 
## -0.1708333
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_nn1.n3_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n3_test),-0.01,0.01)
bst_tda_kde_5.40.5_nn1.n3_test
## $probLeft
## [1] 0.5
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_nn1.n3_test_odds.left<-bst_tda_kde_5.40.5_nn1.n3_test$probLeft/bst_tda_kde_5.40.5_nn1.n3_test$probRight
bst_tda_kde_5.40.5_nn1.n3_test_odds.left
## [1] Inf
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_nn1.n3_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n3_test),-0.01,0.01)
bsr_tda_kde_5.40.5_nn1.n3_test
## $winLeft
## [1] 0.8394667
## 
## $winRope
## [1] 0.1605333
## 
## $winRight
## [1] 0
# Bayesian Correlated Test

bct_tda_kde_5.40.5_nn1.n3_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n3_test),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_nn1.n3_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_nn1.n3_test)))

#BayesFactor
#bf_tda_kde_5.40.5_nn1.n3_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n3_test)) #bf_tda_pca_5.40.5_nn1.n3_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n3_test))

##Node4

#Neural Network 1
DryBean_TDA_KDE_5.40.5_n4_NN1Fit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.40.5.n4.vec, 
                           Importance = T,
                     method = 'nnet', 
                     trControl = fitControl,
                     tuneGrid = nn1Grid,
                     metric='Accuracy')
## # weights:  46
## initial  value 1481.368366 
## iter  10 value 1209.375420
## iter  20 value 1208.237419
## iter  30 value 1195.537485
## iter  40 value 1072.247248
## iter  50 value 1064.886303
## iter  60 value 1056.225639
## iter  70 value 837.945103
## iter  80 value 669.256476
## iter  90 value 662.291500
## iter 100 value 649.783542
## final  value 649.783542 
## stopped after 100 iterations
## # weights:  67
## initial  value 2378.693702 
## iter  10 value 1224.582047
## iter  20 value 1209.145849
## iter  30 value 1208.304698
## iter  40 value 1208.007923
## iter  50 value 1207.594594
## iter  60 value 1207.205077
## iter  70 value 1207.201249
## final  value 1207.201230 
## converged
## # weights:  109
## initial  value 1519.900897 
## iter  10 value 1208.623236
## iter  20 value 1208.254807
## iter  30 value 1079.347076
## iter  40 value 785.405701
## iter  50 value 682.103962
## iter  60 value 662.893865
## iter  70 value 652.748038
## iter  80 value 641.460269
## iter  90 value 640.338921
## iter 100 value 640.221158
## final  value 640.221158 
## stopped after 100 iterations
## # weights:  151
## initial  value 2038.407245 
## iter  10 value 1213.642837
## iter  20 value 1206.885170
## iter  30 value 993.515613
## iter  40 value 812.219885
## iter  50 value 719.734796
## iter  60 value 690.985485
## iter  70 value 650.662775
## iter  80 value 604.807993
## iter  90 value 566.227069
## iter 100 value 549.643156
## final  value 549.643156 
## stopped after 100 iterations
## # weights:  46
## initial  value 1381.153590 
## iter  10 value 1215.951777
## iter  20 value 1208.774466
## iter  30 value 1208.530465
## final  value 1208.526671 
## converged
## # weights:  67
## initial  value 1679.748983 
## iter  10 value 1211.586116
## iter  20 value 1209.236355
## iter  30 value 1208.532875
## iter  40 value 1208.416480
## iter  50 value 1208.141165
## iter  60 value 1205.517213
## iter  70 value 1197.434823
## iter  80 value 1083.156342
## iter  90 value 845.343774
## iter 100 value 731.316718
## final  value 731.316718 
## stopped after 100 iterations
## # weights:  109
## initial  value 1751.868987 
## iter  10 value 1223.108155
## iter  20 value 1211.250690
## iter  30 value 1209.668682
## iter  40 value 1166.251999
## iter  50 value 860.836081
## iter  60 value 760.266675
## iter  70 value 730.381879
## iter  80 value 708.470441
## iter  90 value 679.705477
## iter 100 value 657.899224
## final  value 657.899224 
## stopped after 100 iterations
## # weights:  151
## initial  value 1945.176703 
## iter  10 value 1210.599742
## iter  20 value 1207.597244
## iter  30 value 1207.329538
## iter  40 value 1207.279425
## iter  50 value 1107.574043
## iter  60 value 1080.655296
## iter  70 value 1023.165168
## iter  80 value 715.341726
## iter  90 value 638.085419
## iter 100 value 582.152385
## final  value 582.152385 
## stopped after 100 iterations
## # weights:  46
## initial  value 1887.741462 
## iter  10 value 1243.736045
## iter  20 value 1237.909437
## iter  30 value 1171.769626
## iter  40 value 852.184319
## iter  50 value 727.771114
## iter  60 value 707.777158
## iter  70 value 675.212027
## iter  80 value 665.477204
## iter  90 value 664.719079
## iter 100 value 664.548578
## final  value 664.548578 
## stopped after 100 iterations
## # weights:  67
## initial  value 1441.600930 
## iter  10 value 1210.221130
## iter  20 value 1209.462802
## iter  30 value 1209.447767
## iter  30 value 1209.447758
## iter  40 value 1208.663458
## final  value 1208.644256 
## converged
## # weights:  109
## initial  value 2424.444203 
## iter  10 value 1219.081283
## iter  20 value 1177.449358
## iter  30 value 1078.071644
## iter  40 value 1048.408084
## iter  50 value 934.608793
## iter  60 value 854.575887
## iter  70 value 721.560730
## iter  80 value 688.432632
## iter  90 value 671.125231
## iter 100 value 665.338223
## final  value 665.338223 
## stopped after 100 iterations
## # weights:  151
## initial  value 2515.029150 
## iter  10 value 1228.355558
## iter  20 value 1214.370088
## iter  30 value 1193.893772
## iter  40 value 1184.330659
## iter  50 value 807.702867
## iter  60 value 627.897221
## iter  70 value 595.081331
## iter  80 value 557.929926
## iter  90 value 547.331051
## iter 100 value 541.083673
## final  value 541.083673 
## stopped after 100 iterations
## # weights:  46
## initial  value 2016.481980 
## iter  10 value 1206.950801
## iter  20 value 1206.247077
## final  value 1206.246965 
## converged
## # weights:  67
## initial  value 1575.402751 
## iter  10 value 1210.987577
## iter  20 value 1207.089862
## iter  30 value 1206.963154
## iter  40 value 1206.113706
## iter  50 value 1205.908051
## iter  60 value 1026.238513
## iter  70 value 831.690111
## iter  80 value 741.298683
## iter  90 value 733.136381
## iter 100 value 684.938859
## final  value 684.938859 
## stopped after 100 iterations
## # weights:  109
## initial  value 1760.690789 
## iter  10 value 1236.997493
## iter  20 value 1236.979357
## iter  20 value 1236.979350
## iter  30 value 1209.133758
## iter  40 value 1205.960572
## iter  50 value 1205.760557
## iter  60 value 1205.445671
## iter  70 value 1165.052523
## iter  80 value 1033.912162
## iter  90 value 844.539949
## iter 100 value 765.790718
## final  value 765.790718 
## stopped after 100 iterations
## # weights:  151
## initial  value 2034.510763 
## iter  10 value 1208.273859
## iter  20 value 1206.008749
## iter  30 value 1196.417735
## iter  40 value 822.693553
## iter  50 value 743.360827
## iter  60 value 703.089303
## iter  70 value 685.431434
## iter  80 value 681.989757
## iter  90 value 670.218535
## iter 100 value 621.636589
## final  value 621.636589 
## stopped after 100 iterations
## # weights:  46
## initial  value 1869.062957 
## iter  10 value 1210.256736
## iter  20 value 1207.258736
## iter  30 value 1207.203626
## final  value 1207.203410 
## converged
## # weights:  67
## initial  value 1936.568802 
## iter  10 value 1210.251509
## iter  20 value 1207.286811
## iter  30 value 1207.188957
## iter  40 value 983.199836
## iter  50 value 858.408611
## iter  60 value 833.468990
## iter  70 value 767.863821
## iter  80 value 724.411446
## iter  90 value 700.215003
## iter 100 value 669.702005
## final  value 669.702005 
## stopped after 100 iterations
## # weights:  109
## initial  value 1544.790044 
## iter  10 value 1220.508013
## iter  20 value 1208.866641
## iter  30 value 1208.359925
## final  value 1208.351250 
## converged
## # weights:  151
## initial  value 2155.406904 
## iter  10 value 1210.856326
## iter  20 value 1207.788439
## iter  30 value 1206.493582
## iter  40 value 1206.412979
## iter  50 value 1060.373528
## iter  60 value 1047.577558
## iter  70 value 903.415823
## iter  80 value 749.608038
## iter  90 value 733.321993
## iter 100 value 703.371786
## final  value 703.371786 
## stopped after 100 iterations
## # weights:  46
## initial  value 2058.922940 
## iter  10 value 1210.061637
## iter  20 value 1208.570845
## iter  30 value 1208.126104
## final  value 1208.125852 
## converged
## # weights:  67
## initial  value 1459.299395 
## iter  10 value 1208.940342
## iter  20 value 1035.950709
## iter  30 value 824.002335
## iter  40 value 771.330078
## iter  50 value 763.846861
## iter  60 value 724.716992
## iter  70 value 678.506797
## iter  80 value 675.203629
## final  value 675.197569 
## converged
## # weights:  109
## initial  value 1709.890868 
## iter  10 value 1217.872656
## iter  20 value 1214.053366
## iter  30 value 1208.825929
## iter  40 value 1208.073991
## iter  50 value 1206.578243
## iter  60 value 1128.023553
## iter  70 value 1051.383082
## iter  80 value 1018.661980
## iter  90 value 999.588183
## iter 100 value 841.328496
## final  value 841.328496 
## stopped after 100 iterations
## # weights:  151
## initial  value 1589.655968 
## iter  10 value 1212.897757
## iter  20 value 1208.322406
## iter  30 value 1202.858186
## iter  40 value 1102.881106
## iter  50 value 881.266449
## iter  60 value 777.425164
## iter  70 value 751.608516
## iter  80 value 699.626918
## iter  90 value 667.482239
## iter 100 value 626.304747
## final  value 626.304747 
## stopped after 100 iterations
## # weights:  46
## initial  value 1461.162396 
## iter  10 value 1208.565596
## iter  20 value 1208.292260
## iter  30 value 1208.171268
## iter  40 value 1094.531374
## iter  50 value 1055.963514
## iter  60 value 1035.046176
## iter  70 value 930.471429
## iter  80 value 718.621850
## iter  90 value 690.925457
## iter 100 value 681.731734
## final  value 681.731734 
## stopped after 100 iterations
## # weights:  67
## initial  value 1314.767323 
## iter  10 value 1211.025800
## iter  20 value 1208.363756
## iter  30 value 1208.290894
## iter  40 value 1207.588257
## iter  50 value 1207.518015
## iter  60 value 1207.243697
## iter  70 value 1207.215084
## iter  80 value 1207.203281
## final  value 1207.202671 
## converged
## # weights:  109
## initial  value 1536.975206 
## iter  10 value 1208.809801
## iter  20 value 1207.220495
## iter  30 value 1206.830329
## final  value 1206.826711 
## converged
## # weights:  151
## initial  value 2151.092876 
## iter  10 value 1207.531084
## iter  20 value 1206.884980
## iter  30 value 1206.754029
## iter  40 value 1055.360644
## iter  50 value 930.186318
## iter  60 value 821.567957
## iter  70 value 706.870520
## iter  80 value 631.250784
## iter  90 value 629.670129
## iter 100 value 614.613056
## final  value 614.613056 
## stopped after 100 iterations
## # weights:  46
## initial  value 1443.798334 
## iter  10 value 1210.735437
## iter  20 value 1209.687741
## iter  30 value 1209.675110
## iter  40 value 1122.408289
## iter  50 value 756.656179
## iter  60 value 694.578488
## iter  70 value 679.459045
## iter  80 value 664.468939
## iter  90 value 664.254557
## final  value 664.254401 
## converged
## # weights:  67
## initial  value 1940.221845 
## iter  10 value 1210.520354
## iter  20 value 1208.547137
## iter  30 value 1208.526715
## iter  40 value 1207.917885
## iter  50 value 1187.431581
## iter  60 value 1046.418127
## iter  70 value 1021.743401
## iter  80 value 904.580392
## iter  90 value 775.565699
## iter 100 value 697.213070
## final  value 697.213070 
## stopped after 100 iterations
## # weights:  109
## initial  value 2199.062502 
## iter  10 value 1224.422827
## iter  20 value 1210.778512
## iter  30 value 1209.252858
## iter  40 value 1207.330867
## iter  50 value 1172.831998
## iter  60 value 1076.810739
## iter  70 value 809.867780
## iter  80 value 682.998634
## iter  90 value 642.237385
## iter 100 value 610.330356
## final  value 610.330356 
## stopped after 100 iterations
## # weights:  151
## initial  value 2753.334631 
## iter  10 value 1244.652576
## iter  20 value 1209.692524
## iter  30 value 1207.087680
## iter  40 value 1207.015324
## final  value 1207.015148 
## converged
## # weights:  46
## initial  value 1809.637317 
## iter  10 value 1226.762382
## iter  20 value 1221.809275
## iter  30 value 1215.466266
## iter  40 value 943.217459
## iter  50 value 740.091878
## iter  60 value 697.433318
## iter  70 value 673.212259
## iter  80 value 655.418627
## iter  90 value 636.009744
## iter 100 value 603.606825
## final  value 603.606825 
## stopped after 100 iterations
## # weights:  67
## initial  value 2037.992053 
## iter  10 value 1225.525130
## iter  20 value 1211.617976
## iter  30 value 1209.637629
## iter  40 value 1204.357500
## iter  50 value 945.823938
## iter  60 value 770.163777
## iter  70 value 686.641294
## iter  80 value 675.096897
## iter  90 value 673.709825
## iter 100 value 673.219481
## final  value 673.219481 
## stopped after 100 iterations
## # weights:  109
## initial  value 1954.676031 
## iter  10 value 1216.886855
## iter  20 value 1210.204346
## iter  30 value 1197.170353
## iter  40 value 1052.897884
## iter  50 value 1031.270218
## iter  60 value 871.545119
## iter  70 value 813.058131
## iter  80 value 766.243450
## iter  90 value 674.367573
## iter 100 value 608.784466
## final  value 608.784466 
## stopped after 100 iterations
## # weights:  151
## initial  value 2214.511346 
## iter  10 value 1240.138925
## iter  20 value 1210.986358
## iter  30 value 1210.860875
## iter  40 value 1210.608132
## iter  50 value 1208.812365
## iter  60 value 1208.077988
## iter  70 value 1207.065346
## iter  80 value 1189.622008
## iter  90 value 789.688072
## iter 100 value 703.905415
## final  value 703.905415 
## stopped after 100 iterations
## # weights:  151
## initial  value 3029.702119 
## iter  10 value 1832.421216
## iter  20 value 1819.124667
## iter  30 value 1815.414467
## iter  40 value 1276.957132
## iter  50 value 1069.215378
## iter  60 value 1049.135952
## iter  70 value 1035.268886
## iter  80 value 1014.852261
## iter  90 value 1008.481524
## iter 100 value 968.804373
## final  value 968.804373 
## stopped after 100 iterations
DryBean_TDA_KDE_5.40.5_n4_NN1Fit0
## Neural Network 
## 
## 1759 samples
##   16 predictor
##    4 classes: 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 1173, 1172, 1173 
## Resampling results across tuning parameters:
## 
##   size  decay  Accuracy   Kappa    
##   2     0.3    0.6874110  0.3774863
##   2     0.5    0.6123256  0.1928286
##   2     0.7    0.6868421  0.3761985
##   3     0.3    0.6133247  0.1948714
##   3     0.5    0.7640632  0.5610887
##   3     0.7    0.6923938  0.3856020
##   5     0.3    0.6867104  0.3771300
##   5     0.5    0.6908239  0.3850612
##   5     0.7    0.7663424  0.5832126
##   7     0.3    0.7771550  0.6196315
##   7     0.5    0.7066135  0.4221853
##   7     0.7    0.7828326  0.6136298
## 
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 7 and decay = 0.7.
DryBean_TDA_KDE_5.40.5_n4_NN1Fit0$resample
##    Accuracy     Kappa Resample
## 1 0.7802385 0.6026666    Fold2
## 2 0.8037543 0.6774363    Fold1
## 3 0.7645051 0.5607864    Fold3
nb_tda_kde_5.40.5_n4_nn1_fit_re<-DryBean_TDA_KDE_5.40.5_n4_NN1Fit0$resample[1]

summary(DryBean_TDA_KDE_5.40.5_n4_NN1Fit0)
## a 16-7-4 network with 151 weights
## options were - softmax modelling  decay=0.7
##   b->h1  i1->h1  i2->h1  i3->h1  i4->h1  i5->h1  i6->h1  i7->h1  i8->h1  i9->h1 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
## i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1 i16->h1 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h2  i1->h2  i2->h2  i3->h2  i4->h2  i5->h2  i6->h2  i7->h2  i8->h2  i9->h2 
##    0.00    0.29    0.01    0.00    0.00    0.00    0.00    0.30    0.00    0.00 
## i10->h2 i11->h2 i12->h2 i13->h2 i14->h2 i15->h2 i16->h2 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h3  i1->h3  i2->h3  i3->h3  i4->h3  i5->h3  i6->h3  i7->h3  i8->h3  i9->h3 
##    0.00    0.07    0.00    0.00    0.00    0.00    0.00    0.07    0.00    0.00 
## i10->h3 i11->h3 i12->h3 i13->h3 i14->h3 i15->h3 i16->h3 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h4  i1->h4  i2->h4  i3->h4  i4->h4  i5->h4  i6->h4  i7->h4  i8->h4  i9->h4 
##    0.00    0.01    0.00    0.00    0.00    0.00    0.00    0.01    0.00    0.00 
## i10->h4 i11->h4 i12->h4 i13->h4 i14->h4 i15->h4 i16->h4 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h5  i1->h5  i2->h5  i3->h5  i4->h5  i5->h5  i6->h5  i7->h5  i8->h5  i9->h5 
##   -0.02   -0.01   -0.06    1.04    1.05   -0.02    0.00    0.01   -1.86   -0.13 
## i10->h5 i11->h5 i12->h5 i13->h5 i14->h5 i15->h5 i16->h5 
##   -0.02   -0.03   -0.03    0.00    0.00   -0.04   -0.05 
##   b->h6  i1->h6  i2->h6  i3->h6  i4->h6  i5->h6  i6->h6  i7->h6  i8->h6  i9->h6 
##   -0.01   -0.03    0.40   -0.76   -0.18   -0.01   -0.01    0.03   -0.48    0.01 
## i10->h6 i11->h6 i12->h6 i13->h6 i14->h6 i15->h6 i16->h6 
##   -0.01   -0.01    0.00    0.00    0.00    0.00   -0.01 
##   b->h7  i1->h7  i2->h7  i3->h7  i4->h7  i5->h7  i6->h7  i7->h7  i8->h7  i9->h7 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
## i10->h7 i11->h7 i12->h7 i13->h7 i14->h7 i15->h7 i16->h7 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##  b->o1 h1->o1 h2->o1 h3->o1 h4->o1 h5->o1 h6->o1 h7->o1 
##   0.04   0.04   0.04   0.04   0.04   2.95  -0.37   0.04 
##  b->o2 h1->o2 h2->o2 h3->o2 h4->o2 h5->o2 h6->o2 h7->o2 
##  -0.39  -0.39  -0.39  -0.39  -0.39   0.53  -0.33  -0.39 
##  b->o3 h1->o3 h2->o3 h3->o3 h4->o3 h5->o3 h6->o3 h7->o3 
##   0.55   0.55   0.55   0.55   0.55  -6.72   0.74   0.55 
##  b->o4 h1->o4 h2->o4 h3->o4 h4->o4 h5->o4 h6->o4 h7->o4 
##  -0.20  -0.20  -0.20  -0.20  -0.20   3.23  -0.04  -0.20
#vip(DryBean_TDA_KDE_5.40.5_n4_NN1Fit0,50) + ggtitle("DryBean_TDA_KDE_5.40.5_n4_NN1Fit TDA-Assited NN")
 

# Predict outcome using DryBean_TDA_KDE_5.40.5_n4_NN1Fit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.40.5_n4_NN1Fit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.40.5_n4_db_nn1_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length

## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
nb_tda_kde_5.40.5_n4_db_nn1_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     0    0
##   DERMASON      319     44  483     1042   578    34  772
##   HOROZ           0      0    0        0     0     0    0
##   SEKER          77    112    6       21     0   574   18
##   SIRA            0      0    0        0     0     0    0
## 
## Overall Statistics
##                                          
##                Accuracy : 0.3961         
##                  95% CI : (0.381, 0.4113)
##     No Information Rate : 0.2605         
##     P-Value [Acc > NIR] : < 2.2e-16      
##                                          
##                   Kappa : 0.207          
##                                          
##  Mcnemar's Test P-Value : NA             
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000      0.0000          0.9802
## Specificity                  1.00000       1.00000      1.0000          0.2609
## Pos Pred Value                   NaN           NaN         NaN          0.3185
## Neg Pred Value               0.90294       0.96176      0.8801          0.9740
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.00000       0.00000      0.0000          0.2554
## Detection Prevalence         0.00000       0.00000      0.0000          0.8020
## Balanced Accuracy            0.50000       0.50000      0.5000          0.6205
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.0000       0.9441      0.0000
## Specificity                1.0000       0.9326      1.0000
## Pos Pred Value                NaN       0.7104         NaN
## Neg Pred Value             0.8583       0.9896      0.8064
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.0000       0.1407      0.0000
## Detection Prevalence       0.0000       0.1980      0.0000
## Balanced Accuracy          0.5000       0.9383      0.5000
nb_tda_kde_5.40.5_n4_db_nn1_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     0    0
##   DERMASON      319     44  483     1042   578    34  772
##   HOROZ           0      0    0        0     0     0    0
##   SEKER          77    112    6       21     0   574   18
##   SIRA            0      0    0        0     0     0    0
## 
## Overall Statistics
##                                          
##                Accuracy : 0.3961         
##                  95% CI : (0.381, 0.4113)
##     No Information Rate : 0.2605         
##     P-Value [Acc > NIR] : < 2.2e-16      
##                                          
##                   Kappa : 0.207          
##                                          
##  Mcnemar's Test P-Value : NA             
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000      0.0000          0.9802
## Specificity                  1.00000       1.00000      1.0000          0.2609
## Pos Pred Value                   NaN           NaN         NaN          0.3185
## Neg Pred Value               0.90294       0.96176      0.8801          0.9740
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.00000       0.00000      0.0000          0.2554
## Detection Prevalence         0.00000       0.00000      0.0000          0.8020
## Balanced Accuracy            0.50000       0.50000      0.5000          0.6205
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.0000       0.9441      0.0000
## Specificity                1.0000       0.9326      1.0000
## Pos Pred Value                NaN       0.7104         NaN
## Neg Pred Value             0.8583       0.9896      0.8064
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.0000       0.1407      0.0000
## Detection Prevalence       0.0000       0.1980      0.0000
## Balanced Accuracy          0.5000       0.9383      0.5000
nb_tda_kde_5.40.5_n4_db_nn1_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   3.960784e-01   2.069796e-01   3.810276e-01   4.112782e-01   2.605392e-01 
## AccuracyPValue  McnemarPValue 
##   1.403756e-79            NaN
nb_tda_kde_5.40.5_n4_db_nn1_cf0_ov_acc<-nb_tda_kde_5.40.5_n4_db_nn1_cf0$overall[1]
nb_tda_kde_5.40.5_n4_db_nn1_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.0000000   1.0000000            NaN      0.9029412        NA
## Class: BOMBAY     0.0000000   1.0000000            NaN      0.9617647        NA
## Class: CALI       0.0000000   1.0000000            NaN      0.8801471        NA
## Class: DERMASON   0.9802446   0.2608552      0.3184597      0.9740099 0.3184597
## Class: HOROZ      0.0000000   1.0000000            NaN      0.8583333        NA
## Class: SEKER      0.9440789   0.9326037      0.7103960      0.9896088 0.7103960
## Class: SIRA       0.0000000   1.0000000            NaN      0.8063725        NA
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.0000000        NA 0.09705882      0.0000000
## Class: BOMBAY   0.0000000        NA 0.03823529      0.0000000
## Class: CALI     0.0000000        NA 0.11985294      0.0000000
## Class: DERMASON 0.9802446 0.4807382 0.26053922      0.2553922
## Class: HOROZ    0.0000000        NA 0.14166667      0.0000000
## Class: SEKER    0.9440789 0.8107345 0.14901961      0.1406863
## Class: SIRA     0.0000000        NA 0.19362745      0.0000000
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA            0.0000000         0.5000000
## Class: BOMBAY              0.0000000         0.5000000
## Class: CALI                0.0000000         0.5000000
## Class: DERMASON            0.8019608         0.6205499
## Class: HOROZ               0.0000000         0.5000000
## Class: SEKER               0.1980392         0.9383413
## Class: SIRA                0.0000000         0.5000000
nb_tda_kde_5.40.5_n4_db_nn1_cf0_pre_rec_f1<-nb_tda_kde_5.40.5_n4_db_nn1_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_kde_5.40.5_nn1_n4_3_fold<-(db_nn1_fit_re - nb_tda_kde_5.40.5_n4_nn1_fit_re)
diff_drybean_tda_kde_5.40.5_nn1_n4_3_fold
##     Accuracy
## 1 -0.3058916
## 2 -0.0643775
## 3 -0.5041967
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_nn1.n4_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n4_3_fold),-0.01,0.01)
bst_tda_kde_5.40.5_nn1.n4_3_fold
## $probLeft
## [1] 0.75
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_nn1.n4_3_fold_odds.left<-bst_tda_kde_5.40.5_nn1.n4_3_fold$probLeft/bst_tda_kde_5.40.5_nn1.n4_3_fold$probRight
bst_tda_kde_5.40.5_nn1.n4_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_nn1.n4_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n4_3_fold),-0.01,0.01)
bsr_tda_kde_5.40.5_nn1.n4_3_fold
## $winLeft
## [1] 0.9917667
## 
## $winRope
## [1] 0.008233333
## 
## $winRight
## [1] 0
# Bayesian Correlated Test

bct_tda_kde_5.40.5_nn1.n4_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n4_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_nn1.n4_3_fold
## $left
## [1] 0.9023549
## 
## $rope
## [1] 0.009415571
## 
## $right
## [1] 0.08822951
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.40.5_nn1_n4_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_kde_5.40.5_nn1.n4_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n4_3_fold))
#bf_tda_kde_5.40.5_nn1.n4_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n4_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n4_3_fold)
## t = -2.2921, df = 2, p-value = 0.149
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.8386523  0.2556751
## sample estimates:
##  mean of x 
## -0.2914886
### Test set diff
diff_drybean_tda_kde_5.40.5_nn1.n4_test<-(db_nn1_cf_ov_acc - nb_tda_kde_5.40.5_n4_db_nn1_cf0_ov_acc)
diff_drybean_tda_kde_5.40.5_nn1.n4_test
##    Accuracy 
## -0.01813725
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_nn1.n4_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n4_test),-0.01,0.01)
bst_tda_kde_5.40.5_nn1.n4_test
## $probLeft
## [1] 0.5
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_nn1.n4_test_odds.left<-bst_tda_kde_5.40.5_nn1.n4_test$probLeft/bst_tda_kde_5.40.5_nn1.n4_test$probRight
bst_tda_kde_5.40.5_nn1.n4_test_odds.left
## [1] Inf
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_nn1.n4_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n4_test),-0.01,0.01)
bsr_tda_kde_5.40.5_nn1.n4_test
## $winLeft
## [1] 0.5405333
## 
## $winRope
## [1] 0.4594667
## 
## $winRight
## [1] 0
# Bayesian Correlated Test

bct_tda_kde_5.40.5_nn1.n4_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n4_test),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_nn1.n4_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_nn1.n4_test)))

#BayesFactor
#bf_tda_kde_5.40.5_nn1.n4_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n4_test)) #bf_tda_pca_5.40.5_nn1.n4_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n4_test))

##Node5

#Neural Network 1

DryBean_TDA_KDE_5.40.5_n5_NN1Fit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.40.5.n5.vec, 
                           Importance = T,
                     method = 'nnet', 
                     trControl = fitControl,
                     tuneGrid = nn1Grid,
                     metric='Accuracy')
## # weights:  46
## initial  value 920.050666 
## iter  10 value 518.522091
## iter  20 value 518.181033
## iter  30 value 516.421330
## iter  40 value 500.595578
## iter  50 value 474.432415
## iter  60 value 470.314197
## iter  70 value 434.859474
## iter  80 value 361.523510
## iter  90 value 347.385125
## iter 100 value 331.220949
## final  value 331.220949 
## stopped after 100 iterations
## # weights:  67
## initial  value 710.807796 
## iter  10 value 517.074866
## iter  20 value 516.540055
## iter  30 value 516.313721
## final  value 516.305750 
## converged
## # weights:  109
## initial  value 608.777938 
## iter  10 value 516.501836
## iter  20 value 516.102579
## iter  30 value 516.062605
## iter  40 value 506.607225
## iter  50 value 415.667486
## iter  60 value 385.825745
## iter  70 value 357.169086
## iter  80 value 352.901336
## iter  90 value 335.803684
## iter 100 value 325.255891
## final  value 325.255891 
## stopped after 100 iterations
## # weights:  151
## initial  value 751.236047 
## iter  10 value 522.235375
## iter  20 value 516.754405
## iter  30 value 516.174422
## iter  40 value 516.149161
## iter  50 value 484.161026
## iter  60 value 385.297357
## iter  70 value 341.455014
## iter  80 value 332.533680
## iter  90 value 328.686299
## iter 100 value 319.942398
## final  value 319.942398 
## stopped after 100 iterations
## # weights:  46
## initial  value 919.513179 
## iter  10 value 519.165414
## iter  20 value 518.816057
## iter  30 value 517.790874
## iter  40 value 517.724850
## final  value 517.724707 
## converged
## # weights:  67
## initial  value 691.273745 
## iter  10 value 520.221267
## iter  20 value 517.965114
## iter  30 value 517.362140
## iter  40 value 496.388540
## iter  50 value 361.358976
## iter  60 value 324.616398
## iter  70 value 319.335045
## iter  80 value 314.925221
## iter  90 value 314.670600
## final  value 314.670503 
## converged
## # weights:  109
## initial  value 734.734501 
## iter  10 value 523.461080
## iter  20 value 517.281541
## iter  30 value 516.487240
## iter  40 value 515.154774
## iter  50 value 504.533290
## iter  60 value 398.854836
## iter  70 value 386.920960
## iter  80 value 339.672375
## iter  90 value 328.972834
## iter 100 value 325.326220
## final  value 325.326220 
## stopped after 100 iterations
## # weights:  151
## initial  value 755.221033 
## iter  10 value 526.510247
## iter  20 value 517.653619
## iter  30 value 517.114870
## iter  40 value 516.750950
## iter  50 value 515.996181
## iter  60 value 510.860188
## iter  70 value 403.303173
## iter  80 value 364.546333
## iter  90 value 346.805371
## iter 100 value 321.205189
## final  value 321.205189 
## stopped after 100 iterations
## # weights:  46
## initial  value 738.947612 
## iter  10 value 521.603621
## iter  20 value 520.570550
## iter  30 value 517.895143
## iter  40 value 495.492163
## iter  50 value 367.336869
## iter  60 value 359.313410
## iter  70 value 351.738818
## iter  80 value 339.575115
## iter  90 value 326.085423
## iter 100 value 322.438794
## final  value 322.438794 
## stopped after 100 iterations
## # weights:  67
## initial  value 1011.336161 
## iter  10 value 528.099930
## iter  20 value 523.501835
## iter  30 value 517.935482
## iter  40 value 517.874167
## iter  50 value 517.858450
## iter  60 value 517.300248
## iter  70 value 512.142429
## iter  80 value 501.802660
## iter  90 value 371.369194
## iter 100 value 347.398235
## final  value 347.398235 
## stopped after 100 iterations
## # weights:  109
## initial  value 605.338978 
## iter  10 value 518.752856
## iter  20 value 517.369664
## iter  30 value 517.153675
## iter  40 value 493.924463
## iter  50 value 488.200850
## iter  60 value 448.618675
## iter  70 value 387.774096
## iter  80 value 340.246226
## iter  90 value 339.236636
## iter 100 value 334.842410
## final  value 334.842410 
## stopped after 100 iterations
## # weights:  151
## initial  value 640.989277 
## iter  10 value 523.645792
## iter  20 value 517.304208
## iter  30 value 516.978371
## iter  40 value 516.860018
## iter  50 value 516.663498
## iter  60 value 516.611662
## iter  70 value 516.488362
## iter  80 value 441.845570
## iter  90 value 413.687944
## iter 100 value 380.549113
## final  value 380.549113 
## stopped after 100 iterations
## # weights:  46
## initial  value 720.709277 
## iter  10 value 517.168109
## iter  20 value 515.549838
## iter  30 value 515.108027
## final  value 515.093664 
## converged
## # weights:  67
## initial  value 622.857376 
## iter  10 value 518.024055
## iter  20 value 515.292763
## iter  30 value 514.754444
## iter  40 value 514.687517
## final  value 514.687312 
## converged
## # weights:  109
## initial  value 1030.924103 
## iter  10 value 525.325535
## iter  20 value 515.096788
## iter  30 value 458.493598
## iter  40 value 379.096245
## iter  50 value 359.053891
## iter  60 value 355.019334
## iter  70 value 342.102130
## iter  80 value 341.819360
## iter  90 value 341.812049
## iter  90 value 341.812047
## final  value 341.812047 
## converged
## # weights:  151
## initial  value 749.538779 
## iter  10 value 514.340459
## iter  20 value 514.251981
## iter  30 value 514.214021
## iter  40 value 514.075718
## iter  50 value 514.033541
## iter  60 value 510.526437
## iter  70 value 451.938435
## iter  80 value 365.160051
## iter  90 value 342.764556
## iter 100 value 341.368149
## final  value 341.368149 
## stopped after 100 iterations
## # weights:  46
## initial  value 786.307620 
## iter  10 value 518.521187
## iter  20 value 518.146427
## iter  30 value 507.521915
## iter  40 value 504.441685
## iter  50 value 479.481853
## iter  60 value 423.050476
## iter  70 value 365.987416
## iter  80 value 353.669148
## iter  90 value 351.783125
## final  value 351.780265 
## converged
## # weights:  67
## initial  value 584.199120 
## iter  10 value 516.383976
## iter  20 value 515.503590
## iter  30 value 515.484110
## iter  30 value 515.484110
## iter  30 value 515.484110
## final  value 515.484110 
## converged
## # weights:  109
## initial  value 912.089462 
## iter  10 value 527.945203
## iter  20 value 516.672662
## iter  30 value 516.114628
## iter  40 value 515.394918
## iter  50 value 510.491974
## iter  60 value 409.917784
## iter  70 value 396.241617
## iter  80 value 393.129422
## iter  90 value 364.234465
## iter 100 value 356.210718
## final  value 356.210718 
## stopped after 100 iterations
## # weights:  151
## initial  value 866.185644 
## iter  10 value 519.724327
## iter  20 value 514.763751
## iter  30 value 514.643896
## iter  40 value 514.427894
## iter  50 value 506.912180
## iter  60 value 504.007080
## iter  70 value 454.575500
## iter  80 value 377.741082
## iter  90 value 362.418756
## iter 100 value 353.066814
## final  value 353.066814 
## stopped after 100 iterations
## # weights:  46
## initial  value 773.701428 
## iter  10 value 519.917831
## iter  20 value 518.989576
## iter  30 value 498.051814
## iter  40 value 482.933110
## iter  50 value 481.859145
## iter  60 value 480.846090
## iter  70 value 470.852862
## iter  80 value 420.968463
## iter  90 value 388.626697
## iter 100 value 364.594603
## final  value 364.594603 
## stopped after 100 iterations
## # weights:  67
## initial  value 785.328443 
## iter  10 value 517.106499
## iter  20 value 517.036239
## iter  30 value 516.813894
## iter  40 value 516.229945
## iter  50 value 516.225803
## final  value 516.225495 
## converged
## # weights:  109
## initial  value 780.941533 
## iter  10 value 519.248915
## iter  20 value 518.724060
## iter  30 value 516.385781
## iter  40 value 515.762571
## iter  50 value 474.013165
## iter  60 value 461.420217
## iter  70 value 433.024942
## iter  80 value 381.511198
## iter  90 value 358.696117
## iter 100 value 352.632151
## final  value 352.632151 
## stopped after 100 iterations
## # weights:  151
## initial  value 630.986453 
## iter  10 value 519.009516
## iter  20 value 515.501884
## iter  30 value 515.326981
## iter  40 value 515.106471
## iter  50 value 515.094940
## final  value 515.093983 
## converged
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'HOROZ' is empty
## # weights:  43
## initial  value 592.624304 
## iter  10 value 506.445511
## final  value 506.352625 
## converged
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'HOROZ' is empty
## # weights:  63
## initial  value 522.918205 
## iter  10 value 506.352877
## iter  20 value 506.287501
## iter  30 value 504.337059
## iter  40 value 465.479060
## iter  50 value 426.142158
## iter  60 value 406.058402
## iter  70 value 361.457824
## iter  80 value 350.644134
## iter  90 value 335.546516
## iter 100 value 311.954631
## final  value 311.954631 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'HOROZ' is empty
## # weights:  103
## initial  value 533.611961 
## iter  10 value 506.355731
## iter  20 value 506.352608
## iter  30 value 506.321714
## iter  40 value 506.320316
## iter  50 value 506.295759
## iter  60 value 506.289469
## final  value 506.289402 
## converged
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'HOROZ' is empty
## # weights:  143
## initial  value 602.648084 
## iter  10 value 506.305119
## iter  20 value 506.300949
## iter  30 value 506.289314
## iter  40 value 506.281532
## final  value 506.281494 
## converged
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'HOROZ' is empty
## # weights:  43
## initial  value 577.371850 
## iter  10 value 507.025518
## iter  20 value 506.422789
## iter  30 value 506.415728
## final  value 506.415651 
## converged
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'HOROZ' is empty
## # weights:  63
## initial  value 523.857250 
## iter  10 value 506.607468
## iter  20 value 506.417854
## iter  30 value 506.412553
## iter  40 value 490.671958
## iter  50 value 437.410940
## iter  60 value 381.893002
## iter  70 value 356.975774
## iter  80 value 337.311914
## iter  90 value 320.847730
## iter 100 value 318.316511
## final  value 318.316511 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'HOROZ' is empty
## # weights:  103
## initial  value 751.466904 
## iter  10 value 505.968304
## iter  20 value 500.204697
## iter  30 value 447.764036
## iter  40 value 399.670692
## iter  50 value 359.643250
## iter  60 value 343.032055
## iter  70 value 315.140813
## iter  80 value 304.723066
## iter  90 value 304.351706
## iter 100 value 303.194645
## final  value 303.194645 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'HOROZ' is empty
## # weights:  143
## initial  value 812.708089 
## iter  10 value 507.863844
## iter  20 value 506.497155
## iter  30 value 506.394719
## iter  40 value 506.135864
## iter  50 value 498.307881
## iter  60 value 376.874603
## iter  70 value 350.682040
## iter  80 value 343.348693
## iter  90 value 337.429689
## iter 100 value 329.315612
## final  value 329.315612 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'HOROZ' is empty
## # weights:  43
## initial  value 563.895788 
## iter  10 value 506.479470
## iter  20 value 506.478574
## iter  20 value 506.478570
## iter  20 value 506.478567
## final  value 506.478567 
## converged
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'HOROZ' is empty
## # weights:  63
## initial  value 539.034003 
## iter  10 value 506.691648
## iter  20 value 506.433976
## iter  30 value 506.167183
## iter  40 value 504.245710
## iter  50 value 402.140111
## iter  60 value 365.989207
## iter  70 value 346.712200
## iter  80 value 342.075311
## iter  90 value 336.326709
## iter 100 value 330.765984
## final  value 330.765984 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'HOROZ' is empty
## # weights:  103
## initial  value 530.274616 
## iter  10 value 507.893497
## iter  20 value 438.523142
## iter  30 value 371.934126
## iter  40 value 352.305405
## iter  50 value 344.875949
## iter  60 value 335.978356
## iter  70 value 332.061646
## iter  80 value 328.894661
## iter  90 value 325.773617
## iter 100 value 320.619329
## final  value 320.619329 
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'HOROZ' is empty
## # weights:  143
## initial  value 552.815879 
## iter  10 value 506.489788
## iter  20 value 506.344482
## iter  30 value 445.170035
## iter  40 value 357.714153
## iter  50 value 338.005072
## iter  60 value 335.729750
## iter  70 value 335.619727
## iter  80 value 335.419174
## iter  90 value 333.519543
## iter 100 value 327.835206
## final  value 327.835206 
## stopped after 100 iterations
## # weights:  151
## initial  value 1389.993701 
## iter  10 value 770.781391
## iter  20 value 768.638840
## iter  30 value 768.613223
## iter  40 value 768.610859
## iter  50 value 768.565151
## iter  60 value 768.535425
## iter  70 value 768.514304
## iter  80 value 767.855049
## iter  90 value 695.134118
## iter 100 value 633.531357
## final  value 633.531357 
## stopped after 100 iterations
DryBean_TDA_KDE_5.40.5_n5_NN1Fit0
## Neural Network 
## 
## 774 samples
##  16 predictor
##   4 classes: 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 517, 516, 515 
## Resampling results across tuning parameters:
## 
##   size  decay  Accuracy   Kappa     
##   2     0.3    0.6151838  0.15382628
##   2     0.5    0.6227447  0.16491956
##   2     0.7    0.6591014  0.31877222
##   3     0.3    0.5594621  0.05978913
##   3     0.5    0.6048677  0.20298822
##   3     0.7    0.6048577  0.20290226
##   5     0.3    0.6668433  0.30712130
##   5     0.5    0.6552903  0.36510800
##   5     0.7    0.6565272  0.36617264
##   7     0.3    0.6681453  0.30387087
##   7     0.5    0.6526563  0.34906882
##   7     0.7    0.5931946  0.18279146
## 
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 7 and decay = 0.3.
DryBean_TDA_KDE_5.40.5_n5_NN1Fit0$resample
##    Accuracy     Kappa Resample
## 1 0.7325581 0.4899725    Fold2
## 2 0.7081712 0.4216401    Fold1
## 3 0.5637066 0.0000000    Fold3
nb_tda_kde_5.40.5_n5_nn1_fit_re<-DryBean_TDA_KDE_5.40.5_n5_NN1Fit0$resample[1]

summary(DryBean_TDA_KDE_5.40.5_n5_NN1Fit0)
## a 16-7-4 network with 151 weights
## options were - softmax modelling  decay=0.3
##   b->h1  i1->h1  i2->h1  i3->h1  i4->h1  i5->h1  i6->h1  i7->h1  i8->h1  i9->h1 
##    0.00   -0.03    0.00    0.00    0.00    0.00    0.00   -0.03    0.00    0.00 
## i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1 i16->h1 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h2  i1->h2  i2->h2  i3->h2  i4->h2  i5->h2  i6->h2  i7->h2  i8->h2  i9->h2 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
## i10->h2 i11->h2 i12->h2 i13->h2 i14->h2 i15->h2 i16->h2 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h3  i1->h3  i2->h3  i3->h3  i4->h3  i5->h3  i6->h3  i7->h3  i8->h3  i9->h3 
##    0.00    0.04    0.02   -1.61   -0.17    0.02   -0.02   -0.02   -0.61   -0.04 
## i10->h3 i11->h3 i12->h3 i13->h3 i14->h3 i15->h3 i16->h3 
##    0.00   -0.01    0.00    0.00    0.00    0.01    0.00 
##   b->h4  i1->h4  i2->h4  i3->h4  i4->h4  i5->h4  i6->h4  i7->h4  i8->h4  i9->h4 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
## i10->h4 i11->h4 i12->h4 i13->h4 i14->h4 i15->h4 i16->h4 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h5  i1->h5  i2->h5  i3->h5  i4->h5  i5->h5  i6->h5  i7->h5  i8->h5  i9->h5 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
## i10->h5 i11->h5 i12->h5 i13->h5 i14->h5 i15->h5 i16->h5 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h6  i1->h6  i2->h6  i3->h6  i4->h6  i5->h6  i6->h6  i7->h6  i8->h6  i9->h6 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
## i10->h6 i11->h6 i12->h6 i13->h6 i14->h6 i15->h6 i16->h6 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##   b->h7  i1->h7  i2->h7  i3->h7  i4->h7  i5->h7  i6->h7  i7->h7  i8->h7  i9->h7 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
## i10->h7 i11->h7 i12->h7 i13->h7 i14->h7 i15->h7 i16->h7 
##    0.00    0.00    0.00    0.00    0.00    0.00    0.00 
##  b->o1 h1->o1 h2->o1 h3->o1 h4->o1 h5->o1 h6->o1 h7->o1 
##   0.36   0.36   0.36  -0.71   0.36   0.36   0.36   0.36 
##  b->o2 h1->o2 h2->o2 h3->o2 h4->o2 h5->o2 h6->o2 h7->o2 
##  -0.67  -0.67  -0.67  -0.84  -0.67  -0.67  -0.67  -0.67 
##  b->o3 h1->o3 h2->o3 h3->o3 h4->o3 h5->o3 h6->o3 h7->o3 
##   0.06   0.06   0.06   2.12   0.06   0.06   0.06   0.06 
##  b->o4 h1->o4 h2->o4 h3->o4 h4->o4 h5->o4 h6->o4 h7->o4 
##   0.25   0.25   0.25  -0.57   0.25   0.25   0.25   0.25
#vip(DryBean_TDA_KDE_5.40.5_n5_NN1Fit0,50) + ggtitle("DryBean_TDA_KDE_5.40.5_n5_NN1Fit TDA-Assited NN")


# Predict outcome using DryBean_TDA_KDE_5.40.5_n5_NN1Fit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.40.5_n5_NN1Fit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.40.5_n5_db_nn1_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length

## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
nb_tda_kde_5.40.5_n5_db_nn1_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     0    0
##   DERMASON        0      0    0     1011   173    68  161
##   HOROZ           0      0    0        0     0     0    0
##   SEKER         396    156  489       52   405   540  629
##   SIRA            0      0    0        0     0     0    0
## 
## Overall Statistics
##                                           
##                Accuracy : 0.3801          
##                  95% CI : (0.3652, 0.3952)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.237           
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000      0.0000          0.9511
## Specificity                  1.00000       1.00000      1.0000          0.8668
## Pos Pred Value                   NaN           NaN         NaN          0.7155
## Neg Pred Value               0.90294       0.96176      0.8801          0.9805
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.00000       0.00000      0.0000          0.2478
## Detection Prevalence         0.00000       0.00000      0.0000          0.3463
## Balanced Accuracy            0.50000       0.50000      0.5000          0.9089
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.0000       0.8882      0.0000
## Specificity                1.0000       0.3874      1.0000
## Pos Pred Value                NaN       0.2025         NaN
## Neg Pred Value             0.8583       0.9519      0.8064
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.0000       0.1324      0.0000
## Detection Prevalence       0.0000       0.6537      0.0000
## Balanced Accuracy          0.5000       0.6378      0.5000
nb_tda_kde_5.40.5_n5_db_nn1_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     0    0
##   DERMASON        0      0    0     1011   173    68  161
##   HOROZ           0      0    0        0     0     0    0
##   SEKER         396    156  489       52   405   540  629
##   SIRA            0      0    0        0     0     0    0
## 
## Overall Statistics
##                                           
##                Accuracy : 0.3801          
##                  95% CI : (0.3652, 0.3952)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.237           
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000      0.0000          0.9511
## Specificity                  1.00000       1.00000      1.0000          0.8668
## Pos Pred Value                   NaN           NaN         NaN          0.7155
## Neg Pred Value               0.90294       0.96176      0.8801          0.9805
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.00000       0.00000      0.0000          0.2478
## Detection Prevalence         0.00000       0.00000      0.0000          0.3463
## Balanced Accuracy            0.50000       0.50000      0.5000          0.9089
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.0000       0.8882      0.0000
## Specificity                1.0000       0.3874      1.0000
## Pos Pred Value                NaN       0.2025         NaN
## Neg Pred Value             0.8583       0.9519      0.8064
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.0000       0.1324      0.0000
## Detection Prevalence       0.0000       0.6537      0.0000
## Balanced Accuracy          0.5000       0.6378      0.5000
nb_tda_kde_5.40.5_n5_db_nn1_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   3.801471e-01   2.369712e-01   3.652199e-01   3.952461e-01   2.605392e-01 
## AccuracyPValue  McnemarPValue 
##   6.052192e-63            NaN
nb_tda_kde_5.40.5_n5_db_nn1_cf0_ov_acc<-nb_tda_kde_5.40.5_n5_db_nn1_cf0$overall[1]
nb_tda_kde_5.40.5_n5_db_nn1_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.0000000   1.0000000            NaN      0.9029412        NA
## Class: BOMBAY     0.0000000   1.0000000            NaN      0.9617647        NA
## Class: CALI       0.0000000   1.0000000            NaN      0.8801471        NA
## Class: DERMASON   0.9510818   0.8667551      0.7154989      0.9805024 0.7154989
## Class: HOROZ      0.0000000   1.0000000            NaN      0.8583333        NA
## Class: SEKER      0.8881579   0.3873848      0.2024747      0.9518754 0.2024747
## Class: SIRA       0.0000000   1.0000000            NaN      0.8063725        NA
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.0000000        NA 0.09705882      0.0000000
## Class: BOMBAY   0.0000000        NA 0.03823529      0.0000000
## Class: CALI     0.0000000        NA 0.11985294      0.0000000
## Class: DERMASON 0.9510818 0.8166397 0.26053922      0.2477941
## Class: HOROZ    0.0000000        NA 0.14166667      0.0000000
## Class: SEKER    0.8881579 0.3297710 0.14901961      0.1323529
## Class: SIRA     0.0000000        NA 0.19362745      0.0000000
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA            0.0000000         0.5000000
## Class: BOMBAY              0.0000000         0.5000000
## Class: CALI                0.0000000         0.5000000
## Class: DERMASON            0.3463235         0.9089184
## Class: HOROZ               0.0000000         0.5000000
## Class: SEKER               0.6536765         0.6377713
## Class: SIRA                0.0000000         0.5000000
nb_tda_kde_5.40.5_n5_db_nn1_cf0_pre_rec_f1<-nb_tda_kde_5.40.5_n5_db_nn1_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_kde_5.40.5_nn1_n5_3_fold<-(db_nn1_fit_re - nb_tda_kde_5.40.5_n5_nn1_fit_re)
diff_drybean_tda_kde_5.40.5_nn1_n5_3_fold
##      Accuracy
## 1 -0.25821127
## 2  0.03120556
## 3 -0.30339810
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_nn1.n5_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n5_3_fold),-0.01,0.01)
bst_tda_kde_5.40.5_nn1.n5_3_fold
## $probLeft
## [1] 0.5
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0.25
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_nn1.n5_3_fold_odds.left<-bst_tda_kde_5.40.5_nn1.n5_3_fold$probLeft/bst_tda_kde_5.40.5_nn1.n5_3_fold$probRight
bst_tda_kde_5.40.5_nn1.n5_3_fold_odds.left
## [1] 2
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_nn1.n5_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n5_3_fold),-0.01,0.01)
bsr_tda_kde_5.40.5_nn1.n5_3_fold
## $winLeft
## [1] 0.8743
## 
## $winRope
## [1] 0.01496667
## 
## $winRight
## [1] 0.1107333
# Bayesian Correlated Test

bct_tda_kde_5.40.5_nn1.n5_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n5_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_nn1.n5_3_fold
## $left
## [1] 0.8489567
## 
## $rope
## [1] 0.01968622
## 
## $right
## [1] 0.1313571
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.40.5_nn1_n5_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_kde_5.40.5_nn1.n5_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n5_3_fold))
#bf_tda_kde_5.40.5_nn1.n5_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n5_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_kde_5.40.5_nn1_n5_3_fold)
## t = -1.6867, df = 2, p-value = 0.2337
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.6277978  0.2741952
## sample estimates:
##  mean of x 
## -0.1768013
### Test set diff
diff_drybean_tda_kde_5.40.5_nn1.n5_test<-(db_nn1_cf_ov_acc - nb_tda_kde_5.40.5_n5_db_nn1_cf0_ov_acc)
diff_drybean_tda_kde_5.40.5_nn1.n5_test
##     Accuracy 
## -0.002205882
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_nn1.n5_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n5_test),-0.01,0.01)
bst_tda_kde_5.40.5_nn1.n5_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 1
## 
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_nn1.n5_test_odds.left<-bst_tda_kde_5.40.5_nn1.n5_test$probLeft/bst_tda_kde_5.40.5_nn1.n5_test$probRight
bst_tda_kde_5.40.5_nn1.n5_test_odds.left
## [1] NaN
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_nn1.n4_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n4_test),-0.01,0.01)
bsr_tda_kde_5.40.5_nn1.n4_test
## $winLeft
## [1] 0.5433333
## 
## $winRope
## [1] 0.4566667
## 
## $winRight
## [1] 0
# Bayesian Correlated Test

bct_tda_kde_5.40.5_nn1.n5_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n5_test),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_nn1.n5_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_nn1.n5_test)))

#BayesFactor
#bf_tda_kde_5.40.5_nn1.n5_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n5_test)) #bf_tda_pca_5.40.5_nn1.n5_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_nn1.n5_test)) 


##Logistic Regression  method='multinom'

dryBeanLrFit <- train(as.factor(Class) ~ ., 
                 data = Dry_Bean_DatasetTrain, 
                 family = 'binomial',
                method = 'multinom', 
                 trControl = fitControl,
                metric='Accuracy')
## # weights:  126 (102 variable)
## initial  value 12362.367177 
## iter  10 value 9345.294780
## iter  20 value 7164.490167
## iter  30 value 5143.966685
## iter  40 value 2637.302630
## iter  50 value 1416.129507
## iter  60 value 1296.901890
## iter  70 value 1277.016967
## iter  80 value 1256.076175
## iter  90 value 1250.634616
## iter 100 value 1245.972947
## final  value 1245.972947 
## stopped after 100 iterations
## # weights:  126 (102 variable)
## initial  value 12362.367177 
## iter  10 value 9345.294820
## iter  20 value 7164.492258
## iter  30 value 5144.888013
## iter  40 value 2711.347651
## iter  50 value 1765.814169
## iter  60 value 1626.412826
## iter  70 value 1535.518315
## iter  80 value 1480.661820
## iter  90 value 1462.317743
## iter 100 value 1452.306177
## final  value 1452.306177 
## stopped after 100 iterations
## # weights:  126 (102 variable)
## initial  value 12362.367177 
## iter  10 value 9345.294780
## iter  20 value 7164.490169
## iter  30 value 5143.967032
## iter  40 value 2637.381736
## iter  50 value 1417.847737
## iter  60 value 1302.024189
## iter  70 value 1283.259665
## iter  80 value 1265.248521
## iter  90 value 1261.203665
## iter 100 value 1257.682293
## final  value 1257.682293 
## stopped after 100 iterations
## # weights:  126 (102 variable)
## initial  value 12364.313087 
## iter  10 value 9357.236856
## iter  20 value 6855.263499
## iter  30 value 4728.060278
## iter  40 value 2172.226955
## iter  50 value 1351.301105
## iter  60 value 1247.460518
## iter  70 value 1228.221289
## iter  80 value 1213.992099
## iter  90 value 1206.924245
## iter 100 value 1201.728833
## final  value 1201.728833 
## stopped after 100 iterations
## # weights:  126 (102 variable)
## initial  value 12364.313087 
## iter  10 value 9357.236892
## iter  20 value 6855.265377
## iter  30 value 4728.887783
## iter  40 value 2907.595861
## iter  50 value 1689.292071
## iter  60 value 1563.696402
## iter  70 value 1465.106390
## iter  80 value 1414.589536
## iter  90 value 1396.632628
## iter 100 value 1387.267842
## final  value 1387.267842 
## stopped after 100 iterations
## # weights:  126 (102 variable)
## initial  value 12364.313087 
## iter  10 value 9357.236856
## iter  20 value 6855.263501
## iter  30 value 4728.060846
## iter  40 value 2172.354110
## iter  50 value 1352.877009
## iter  60 value 1252.057759
## iter  70 value 1234.443552
## iter  80 value 1222.160246
## iter  90 value 1216.967607
## iter 100 value 1213.287790
## final  value 1213.287790 
## stopped after 100 iterations
## # weights:  126 (102 variable)
## initial  value 12366.258997 
## iter  10 value 9313.217012
## iter  20 value 6781.536950
## iter  30 value 4901.807555
## iter  40 value 2808.689082
## iter  50 value 1412.350788
## iter  60 value 1314.663294
## iter  70 value 1291.256010
## iter  80 value 1268.780063
## iter  90 value 1258.288013
## iter 100 value 1252.420938
## final  value 1252.420938 
## stopped after 100 iterations
## # weights:  126 (102 variable)
## initial  value 12366.258997 
## iter  10 value 9313.217054
## iter  20 value 6781.538729
## iter  30 value 4902.147789
## iter  40 value 2917.749030
## iter  50 value 1719.610167
## iter  60 value 1596.046512
## iter  70 value 1505.166306
## iter  80 value 1456.063534
## iter  90 value 1439.760310
## iter 100 value 1429.239617
## final  value 1429.239617 
## stopped after 100 iterations
## # weights:  126 (102 variable)
## initial  value 12366.258997 
## iter  10 value 9313.217012
## iter  20 value 6781.536948
## iter  30 value 4901.806479
## iter  40 value 2808.778216
## iter  50 value 1413.784935
## iter  60 value 1317.874969
## iter  70 value 1295.832515
## iter  80 value 1276.557913
## iter  90 value 1268.822788
## iter 100 value 1264.497302
## final  value 1264.497302 
## stopped after 100 iterations
## # weights:  126 (102 variable)
## initial  value 18546.469631 
## iter  10 value 14825.707034
## iter  20 value 11515.718830
## iter  30 value 7976.721137
## iter  40 value 4178.679145
## iter  50 value 2052.165749
## iter  60 value 1934.406466
## iter  70 value 1900.891657
## iter  80 value 1880.077018
## iter  90 value 1873.024406
## iter 100 value 1866.696046
## final  value 1866.696046 
## stopped after 100 iterations
dryBeanLrFit
## Penalized Multinomial Regression 
## 
## 9531 samples
##   16 predictor
##    7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 6353, 6354, 6355 
## Resampling results across tuning parameters:
## 
##   decay  Accuracy   Kappa    
##   0e+00  0.9262415  0.9108151
##   1e-04  0.9260314  0.9105678
##   1e-01  0.9223590  0.9061306
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was decay = 0.
dryBeanLrFit$resample
##    Accuracy     Kappa Resample
## 1 0.9310453 0.9166419    Fold3
## 2 0.9241423 0.9082330    Fold2
## 3 0.9235368 0.9075704    Fold1
db_lr_fit_re<-dryBeanLrFit$resample[1]

summary(dryBeanLrFit)
## Call:
## nnet::multinom(formula = .outcome ~ ., data = dat, decay = param$decay, 
##     family = "binomial")
## 
## Coefficients:
##          (Intercept)        Area   Perimeter MajorAxisLength MinorAxisLength
## BOMBAY      6.823199 0.003321315 -0.03476634       0.5953995        1.611803
## CALI       32.464501 0.003449109 -0.17486907       1.9678534        2.698588
## DERMASON   25.857735 0.006480290  0.20707237       0.8242785        1.772622
## HOROZ      15.493026 0.007749273  0.08848830       2.3982504        4.323559
## SEKER     -21.199426 0.009912825  0.15771817      -1.6360872       -3.063281
## SIRA       73.193155 0.004788652 -0.38557142       2.0281782        2.924417
##          AspectRation Eccentricity   ConvexArea EquivDiameter      Extent
## BOMBAY      54.039681     12.24680 -0.001456419     -2.787972 -17.3062579
## CALI       -62.288895    119.72644 -0.003444130     -4.001427  -0.2896988
## DERMASON     8.273777     78.28066 -0.004122469     -4.429458 -18.3328086
## HOROZ        3.646266     90.57945 -0.006219625     -7.582137  -8.0371112
## SEKER       16.267429    -87.47539 -0.008862278      3.479754 -16.1365558
## SIRA       -36.082370    139.77479 -0.004017571     -4.112162 -10.4587654
##           Solidity  roundness Compactness ShapeFactor1 ShapeFactor2
## BOMBAY    8.671012   17.84284   0.5729655    0.6532757   0.12918310
## CALI     28.031562  -38.31424   0.9351259    0.9700331   0.07548658
## DERMASON  8.298269  139.76078   0.6865460    0.7368440  -0.04588692
## HOROZ    37.949736   69.19092 -20.6370979    1.1388649  -0.17336302
## SEKER    -6.119523   91.94795  20.9907986   -0.8258601   0.14420112
## SIRA     47.270315 -141.54437  41.9837921   -0.5919835  -0.21677977
##          ShapeFactor3 ShapeFactor4
## BOMBAY      -1.668027     9.631844
## CALI       -36.413944    -8.096455
## DERMASON   -29.052864     7.674724
## HOROZ      -57.586298     1.235896
## SEKER       67.380723    16.948915
## SIRA        -1.635646    27.525522
## 
## Std. Errors:
##           (Intercept)         Area    Perimeter MajorAxisLength MinorAxisLength
## BOMBAY   9.484071e-07 0.0032941561 0.0004238494    0.0001986851    0.0001076132
## CALI     2.858252e-06 0.0003972297 0.0013439211    0.0007146627    0.0005034093
## DERMASON 8.200319e-06 0.0007865185 0.0025773983    0.0017885053    0.0019666050
## HOROZ    3.947037e-06 0.0005063976 0.0018874697    0.0005775069    0.0006034092
## SEKER    4.923735e-06 0.0010253701 0.0021837569    0.0006953062    0.0006443334
## SIRA     6.340917e-06 0.0005818644 0.0021010939    0.0023283420    0.0024089283
##          AspectRation Eccentricity   ConvexArea EquivDiameter       Extent
## BOMBAY   1.660123e-06 7.903255e-07 0.0032537100  0.0001524253 6.848984e-07
## CALI     5.910173e-06 2.277199e-06 0.0003942677  0.0004144852 2.354548e-06
## DERMASON 1.996073e-05 7.700555e-06 0.0007967773  0.0009055570 7.775731e-06
## HOROZ    5.407329e-06 2.760763e-06 0.0005017110  0.0005625733 3.085227e-06
## SEKER    6.097129e-06 3.182977e-06 0.0010264953  0.0006722176 3.884651e-06
## SIRA     2.486510e-05 8.136608e-06 0.0005835782  0.0008059664 7.034230e-06
##              Solidity    roundness  Compactness ShapeFactor1 ShapeFactor2
## BOMBAY   9.818199e-07 9.676404e-07 7.265826e-07 7.698351e-09 1.652435e-09
## CALI     2.823761e-06 2.781624e-06 2.760759e-06 2.310088e-08 8.914636e-09
## DERMASON 8.107808e-06 9.513165e-06 1.178892e-05 7.147009e-08 6.374348e-08
## HOROZ    3.876048e-06 3.523072e-06 3.630598e-06 3.155145e-08 1.118134e-08
## SEKER    4.881613e-06 4.609670e-06 4.423163e-06 3.993590e-08 1.530821e-08
## SIRA     6.240834e-06 9.305841e-06 1.253697e-05 3.963017e-08 6.690438e-08
##          ShapeFactor3 ShapeFactor4
## BOMBAY   5.558518e-07 9.751691e-07
## CALI     2.765547e-06 2.856756e-06
## DERMASON 1.455604e-05 8.209254e-06
## HOROZ    3.317535e-06 3.932212e-06
## SEKER    3.979397e-06 4.911771e-06
## SIRA     1.644605e-05 6.423743e-06
## 
## Residual Deviance: 3733.392 
## AIC: 3937.392
vip(dryBeanLrFit,25) + ggtitle('non-TDA-Assisted LR')

# Predict outcome using model from training data based on testing data
predictions <- predict(dryBeanLrFit, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
db_lr_cf<-confusionMatrix(data=predictions, as.factor(Dry_Bean_DatasetTest$Class))
db_lr_cf
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      348      0   11        0     1     8    3
##   BOMBAY          1    156    0        0     0     0    0
##   CALI           29      0  463        0     9     0    1
##   DERMASON        0      0    0      978     4     8   65
##   HOROZ           2      0    8        3   558     1   12
##   SEKER           1      0    1       12     0   572   12
##   SIRA           15      0    6       70     6    19  697
## 
## Overall Statistics
##                                          
##                Accuracy : 0.9245         
##                  95% CI : (0.916, 0.9324)
##     No Information Rate : 0.2605         
##     P-Value [Acc > NIR] : < 2.2e-16      
##                                          
##                   Kappa : 0.9087         
##                                          
##  Mcnemar's Test P-Value : NA             
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.87879       1.00000      0.9468          0.9200
## Specificity                  0.99376       0.99975      0.9891          0.9745
## Pos Pred Value               0.93801       0.99363      0.9223          0.9270
## Neg Pred Value               0.98706       1.00000      0.9927          0.9719
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.08529       0.03824      0.1135          0.2397
## Detection Prevalence         0.09093       0.03848      0.1230          0.2586
## Balanced Accuracy            0.93627       0.99987      0.9680          0.9473
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9654       0.9408      0.8823
## Specificity                0.9926       0.9925      0.9647
## Pos Pred Value             0.9555       0.9565      0.8573
## Neg Pred Value             0.9943       0.9897      0.9715
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1368       0.1402      0.1708
## Detection Prevalence       0.1431       0.1466      0.1993
## Balanced Accuracy          0.9790       0.9667      0.9235
db_lr_cf$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.9245098      0.9087052      0.9159732      0.9324324      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
db_lr_cf_ov_acc<-db_lr_cf$overall[1]
db_lr_cf$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.8787879   0.9937568      0.9380054      0.9870585 0.9380054
## Class: BOMBAY     1.0000000   0.9997452      0.9936306      1.0000000 0.9936306
## Class: CALI       0.9468303   0.9891395      0.9223108      0.9927334 0.9223108
## Class: DERMASON   0.9200376   0.9744780      0.9270142      0.9719008 0.9270142
## Class: HOROZ      0.9653979   0.9925757      0.9554795      0.9942792 0.9554795
## Class: SEKER      0.9407895   0.9925115      0.9565217      0.9896611 0.9565217
## Class: SIRA       0.8822785   0.9647416      0.8573186      0.9715335 0.8573186
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.8787879 0.9074316 0.09705882     0.08529412
## Class: BOMBAY   1.0000000 0.9968051 0.03823529     0.03823529
## Class: CALI     0.9468303 0.9344097 0.11985294     0.11348039
## Class: DERMASON 0.9200376 0.9235127 0.26053922     0.23970588
## Class: HOROZ    0.9653979 0.9604131 0.14166667     0.13676471
## Class: SEKER    0.9407895 0.9485904 0.14901961     0.14019608
## Class: SIRA     0.8822785 0.8696195 0.19362745     0.17083333
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA           0.09093137         0.9362723
## Class: BOMBAY             0.03848039         0.9998726
## Class: CALI               0.12303922         0.9679849
## Class: DERMASON           0.25857843         0.9472578
## Class: HOROZ              0.14313725         0.9789868
## Class: SEKER              0.14656863         0.9666505
## Class: SIRA               0.19926471         0.9235101
db_lr_cf_pre_rec_f1<-db_lr_cf$byClass[5:7]


##With TDA PCA filter 5 intervals, 50% overlap, 5 bins 
##Node1

DryBean_TDA_PC_5.40.5_n1_LrFit0 <- multinom(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.40.5.n1.vec, family = 'binomial')
## # weights:  108 (85 variable)
## initial  value 12246.675972 
## iter  10 value 2950.028567
## iter  20 value 2667.760154
## iter  30 value 2184.813614
## iter  40 value 1650.440764
## iter  50 value 1600.491312
## iter  60 value 1584.632550
## iter  70 value 1576.015397
## iter  80 value 1571.201138
## iter  90 value 1562.079765
## iter 100 value 1557.179800
## final  value 1557.179800 
## stopped after 100 iterations
DryBean_TDA_PC_5.40.5_n1_LrFit0 <- train(as.factor(Class) ~ ., 
                 data = tda.m_dry_bean_dataset_5.40.5.n1.vec, 
                    family = 'binomial',
                          method = 'multinom', 
                    trControl = fitControl,
                          metric='Accuracy')
## # weights:  108 (85 variable)
## initial  value 8165.047901 
## iter  10 value 2084.191632
## iter  20 value 1892.856219
## iter  30 value 1543.530132
## iter  40 value 1127.191149
## iter  50 value 1079.353011
## iter  60 value 1062.241923
## iter  70 value 1059.374714
## iter  80 value 1057.580615
## iter  90 value 1054.162633
## iter 100 value 1051.729709
## final  value 1051.729709 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 8165.047901 
## iter  10 value 2084.196518
## iter  20 value 1892.905879
## iter  30 value 1454.729612
## iter  40 value 1150.163087
## iter  50 value 1142.828924
## iter  60 value 1142.493226
## iter  70 value 1142.337746
## iter  80 value 1142.328032
## final  value 1142.327819 
## converged
## # weights:  108 (85 variable)
## initial  value 8165.047901 
## iter  10 value 2084.191637
## iter  20 value 1892.856239
## iter  30 value 1543.573552
## iter  40 value 1127.313596
## iter  50 value 1080.702754
## iter  60 value 1065.106340
## iter  70 value 1062.639910
## iter  80 value 1061.124951
## iter  90 value 1058.688297
## iter 100 value 1057.269924
## final  value 1057.269924 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 8163.256142 
## iter  10 value 2407.574411
## iter  20 value 2238.090435
## iter  30 value 1406.124391
## iter  40 value 1130.326248
## iter  50 value 1086.169805
## iter  60 value 1074.406881
## iter  70 value 1069.387211
## iter  80 value 1066.277177
## iter  90 value 1057.978686
## iter 100 value 1053.852439
## final  value 1053.852439 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 8163.256142 
## iter  10 value 2407.578523
## iter  20 value 2238.102061
## iter  30 value 1429.421290
## iter  40 value 1155.721672
## iter  50 value 1150.682773
## iter  60 value 1149.587772
## iter  70 value 1149.445229
## iter  80 value 1149.428696
## final  value 1149.428566 
## converged
## # weights:  108 (85 variable)
## initial  value 8163.256142 
## iter  10 value 2407.574415
## iter  20 value 2238.090523
## iter  30 value 1406.156914
## iter  40 value 1130.466728
## iter  50 value 1087.791095
## iter  60 value 1077.275585
## iter  70 value 1073.051370
## iter  80 value 1070.539921
## iter  90 value 1065.333604
## iter 100 value 1063.156257
## final  value 1063.156257 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 8165.047901 
## iter  10 value 3157.095970
## iter  20 value 2711.673451
## iter  30 value 1953.040391
## iter  40 value 1136.525804
## iter  50 value 1067.285079
## iter  60 value 1026.614568
## iter  70 value 1011.131999
## iter  80 value 1006.393715
## iter  90 value 1002.760894
## iter 100 value 997.931923
## final  value 997.931923 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 8165.047901 
## iter  10 value 3157.099193
## iter  20 value 2711.703482
## iter  30 value 1632.292787
## iter  40 value 1125.933796
## iter  50 value 1099.597530
## iter  60 value 1096.445647
## iter  70 value 1095.559224
## iter  80 value 1095.381227
## iter  90 value 1095.367646
## final  value 1095.367592 
## converged
## # weights:  108 (85 variable)
## initial  value 8165.047901 
## iter  10 value 3157.095973
## iter  20 value 2711.673516
## iter  30 value 1953.125948
## iter  40 value 1136.523086
## iter  50 value 1067.494533
## iter  60 value 1028.832702
## iter  70 value 1015.294342
## iter  80 value 1011.271586
## iter  90 value 1008.585638
## iter 100 value 1005.290408
## final  value 1005.290408 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 12246.675972 
## iter  10 value 2950.028567
## iter  20 value 2667.760154
## iter  30 value 2184.813614
## iter  40 value 1650.440764
## iter  50 value 1600.491312
## iter  60 value 1584.632550
## iter  70 value 1576.015397
## iter  80 value 1571.201138
## iter  90 value 1562.079765
## iter 100 value 1557.179800
## final  value 1557.179800 
## stopped after 100 iterations
DryBean_TDA_PC_5.40.5_n1_LrFit0
## Penalized Multinomial Regression 
## 
## 6835 samples
##   16 predictor
##    6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 4557, 4556, 4557 
## Resampling results across tuning parameters:
## 
##   decay  Accuracy   Kappa    
##   0e+00  0.9092901  0.8517882
##   1e-04  0.9087051  0.8508882
##   1e-01  0.9008051  0.8375440
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was decay = 0.
DryBean_TDA_PC_5.40.5_n1_LrFit0$resample
##    Accuracy     Kappa Resample
## 1 0.9091308 0.8514165    Fold3
## 2 0.9113646 0.8554961    Fold2
## 3 0.9073749 0.8484520    Fold1
db_tda_pc_5.40.5_n1_lr_fit_re<-DryBean_TDA_PC_5.40.5_n1_LrFit0$resample[1]

summary(DryBean_TDA_PC_5.40.5_n1_LrFit0)
## Call:
## nnet::multinom(formula = .outcome ~ ., data = dat, decay = param$decay, 
##     family = "binomial")
## 
## Coefficients:
##          (Intercept)         Area   Perimeter MajorAxisLength MinorAxisLength
## CALI        14.22785  0.012357549  0.03548497      -4.4483928       -5.598820
## DERMASON    14.70169 -0.002455073  0.21859390       0.9723484        0.886382
## HOROZ      -14.19871 -0.009248312 -0.28194342       0.6316441        0.839296
## SEKER      -31.02954  0.004948707  0.24538583      -1.9284924       -3.936744
## SIRA        59.30604 -0.005368799 -0.46478210       1.8912031        2.276575
##          AspectRation Eccentricity   ConvexArea EquivDiameter    Extent
## CALI        41.767866   -0.1332494 -0.010302694     9.1461559 -45.18848
## DERMASON   -27.812043   46.9011802  0.003411481    -3.3145706 -22.00376
## HOROZ        1.385520   13.7789013  0.004734158     0.7506824 -16.43509
## SEKER        6.274738 -118.7664343 -0.004085662     4.4342313 -19.73911
## SIRA       -67.700724  142.8103097  0.005218742    -2.7288513 -12.40141
##           Solidity   roundness Compactness ShapeFactor1 ShapeFactor2
## CALI      14.26732    3.780069    10.83343    0.3986064   0.08005657
## DERMASON -18.46259  156.328935    11.56775    0.5912817   0.40809953
## HOROZ    -13.87012  -20.471940   -25.37286   -0.1727268  -0.28151570
## SEKER    -16.76536  130.032845    17.99920   -1.2798091   0.04087314
## SIRA      75.98162 -162.203474    32.68422    0.9047131   0.17606113
##          ShapeFactor3 ShapeFactor4
## CALI        10.661592    16.791499
## DERMASON    -2.138718    -2.818280
## HOROZ      -34.344935   -18.489947
## SEKER       72.401117     7.929439
## SIRA        -7.152719    24.915846
## 
## Std. Errors:
##           (Intercept)         Area    Perimeter MajorAxisLength MinorAxisLength
## CALI     5.459956e-07 0.0002815565 0.0003022780    9.028743e-05    4.800852e-05
## DERMASON 6.179195e-06 0.0013048137 0.0010980873    2.300130e-03    2.528687e-03
## HOROZ    1.945524e-07 0.0002458235 0.0001007373    5.081088e-05    1.037292e-05
## SEKER    3.439748e-06 0.0011401299 0.0014249072    5.437337e-04    3.980306e-04
## SIRA     5.095434e-06 0.0012753997 0.0009710998    2.210241e-03    2.290754e-03
##          AspectRation Eccentricity   ConvexArea EquivDiameter       Extent
## CALI     8.548467e-07 4.394388e-07 0.0002772149  6.662733e-05 3.840218e-07
## DERMASON 2.538859e-05 1.000925e-05 0.0012855380  6.786378e-04 6.455734e-06
## HOROZ    5.921749e-07 2.435506e-07 0.0002433418  2.058792e-05 1.090651e-07
## SEKER    5.289977e-06 2.919095e-06 0.0011241735  4.016197e-04 2.697352e-06
## SIRA     2.441907e-05 9.262902e-06 0.0012553742  5.771301e-04 5.531664e-06
##              Solidity    roundness  Compactness ShapeFactor1 ShapeFactor2
## CALI     5.316135e-07 3.518602e-07 4.316824e-07 5.220538e-09 1.408407e-09
## DERMASON 6.105460e-06 9.695235e-06 1.385512e-05 3.593753e-08 8.256440e-08
## HOROZ    1.829085e-07 9.532507e-08 1.000163e-07 2.824102e-09 3.335774e-10
## SEKER    3.409556e-06 3.349817e-06 3.153934e-06 3.291227e-08 1.432030e-08
## SIRA     5.034704e-06 8.169101e-06 1.219814e-05 3.600609e-08 7.227987e-08
##          ShapeFactor3 ShapeFactor4
## CALI     3.386087e-07 5.443332e-07
## DERMASON 1.878599e-05 6.188195e-06
## HOROZ    7.505122e-08 1.898514e-07
## SEKER    3.090575e-06 3.435948e-06
## SIRA     1.673332e-05 5.103272e-06
## 
## Residual Deviance: 3114.36 
## AIC: 3284.36
vip(DryBean_TDA_PC_5.40.5_n1_LrFit0,50) + ggtitle("dryBean_TDA_PCA_5.40.5_n1_Lr1Fit TDA-Assited LR")

# Predict outcome using DryBean_TDA_PC_5.40.5_n1_LrFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.40.5_n1_LrFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.40.5_n1_db_lr_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length

## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
db_tda_pc_5.40.5_n1_db_lr_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA       68      0   23        0     0     3    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI           13    117    1        0     0     0    0
##   DERMASON        8      4    6      994   414     8   77
##   HOROZ           0      0    0        0     0     0    0
##   SEKER         273     35  120       13     3   579   13
##   SIRA           34      0  339       56   161    18  700
## 
## Overall Statistics
##                                           
##                Accuracy : 0.574           
##                  95% CI : (0.5587, 0.5893)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.4659          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.17172       0.00000   0.0020450          0.9351
## Specificity                  0.99294       1.00000   0.9637984          0.8286
## Pos Pred Value               0.72340           NaN   0.0076336          0.6578
## Neg Pred Value               0.91771       0.96176   0.8764244          0.9731
## Prevalence                   0.09706       0.03824   0.1198529          0.2605
## Detection Rate               0.01667       0.00000   0.0002451          0.2436
## Detection Prevalence         0.02304       0.00000   0.0321078          0.3703
## Balanced Accuracy            0.58233       0.50000   0.4829217          0.8819
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.0000       0.9523      0.8861
## Specificity                1.0000       0.8684      0.8152
## Pos Pred Value                NaN       0.5589      0.5352
## Neg Pred Value             0.8583       0.9905      0.9675
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.0000       0.1419      0.1716
## Detection Prevalence       0.0000       0.2539      0.3206
## Balanced Accuracy          0.5000       0.9103      0.8506
db_tda_pc_5.40.5_n1_db_lr_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA       68      0   23        0     0     3    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI           13    117    1        0     0     0    0
##   DERMASON        8      4    6      994   414     8   77
##   HOROZ           0      0    0        0     0     0    0
##   SEKER         273     35  120       13     3   579   13
##   SIRA           34      0  339       56   161    18  700
## 
## Overall Statistics
##                                           
##                Accuracy : 0.574           
##                  95% CI : (0.5587, 0.5893)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.4659          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.17172       0.00000   0.0020450          0.9351
## Specificity                  0.99294       1.00000   0.9637984          0.8286
## Pos Pred Value               0.72340           NaN   0.0076336          0.6578
## Neg Pred Value               0.91771       0.96176   0.8764244          0.9731
## Prevalence                   0.09706       0.03824   0.1198529          0.2605
## Detection Rate               0.01667       0.00000   0.0002451          0.2436
## Detection Prevalence         0.02304       0.00000   0.0321078          0.3703
## Balanced Accuracy            0.58233       0.50000   0.4829217          0.8819
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.0000       0.9523      0.8861
## Specificity                1.0000       0.8684      0.8152
## Pos Pred Value                NaN       0.5589      0.5352
## Neg Pred Value             0.8583       0.9905      0.9675
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.0000       0.1419      0.1716
## Detection Prevalence       0.0000       0.2539      0.3206
## Balanced Accuracy          0.5000       0.9103      0.8506
db_tda_pc_5.40.5_n1_db_lr_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.5740196      0.4658638      0.5586754      0.5892577      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
db_tda_pc_5.40.5_n1_db_lr_cf0_ov_acc<-db_tda_pc_5.40.5_n1_db_lr_cf0$overall[1]
db_tda_pc_5.40.5_n1_db_lr_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value
## Class: BARBUNYA  0.17171717   0.9929425    0.723404255      0.9177120
## Class: BOMBAY    0.00000000   1.0000000            NaN      0.9617647
## Class: CALI      0.00204499   0.9637984    0.007633588      0.8764244
## Class: DERMASON  0.93508937   0.8286377    0.657842488      0.9731413
## Class: HOROZ     0.00000000   1.0000000            NaN      0.8583333
## Class: SEKER     0.95230263   0.8683756    0.558880309      0.9904731
## Class: SIRA      0.88607595   0.8151976    0.535168196      0.9675325
##                   Precision     Recall          F1 Prevalence Detection Rate
## Class: BARBUNYA 0.723404255 0.17171717 0.277551020 0.09705882    0.016666667
## Class: BOMBAY            NA 0.00000000          NA 0.03823529    0.000000000
## Class: CALI     0.007633588 0.00204499 0.003225806 0.11985294    0.000245098
## Class: DERMASON 0.657842488 0.93508937 0.772338772 0.26053922    0.243627451
## Class: HOROZ             NA 0.00000000          NA 0.14166667    0.000000000
## Class: SEKER    0.558880309 0.95230263 0.704379562 0.14901961    0.141911765
## Class: SIRA     0.535168196 0.88607595 0.667302193 0.19362745    0.171568627
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA           0.02303922         0.5823298
## Class: BOMBAY             0.00000000         0.5000000
## Class: CALI               0.03210784         0.4829217
## Class: DERMASON           0.37034314         0.8818635
## Class: HOROZ              0.00000000         0.5000000
## Class: SEKER              0.25392157         0.9103391
## Class: SIRA               0.32058824         0.8506368
db_tda_pc_5.40.5_n1_db_lr_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n1_db_lr_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted LR vs. tda-assisted LR classifiers

### 3-fold diff

diff_drybean_tda_pca_5.40.5_lr_n1_3_fold<-(db_lr_fit_re - db_tda_pc_5.40.5_n1_lr_fit_re)
diff_drybean_tda_pca_5.40.5_lr_n1_3_fold
##     Accuracy
## 1 0.02191452
## 2 0.01277764
## 3 0.01616193
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_lr.n1_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_lr_n1_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_lr.n1_3_fold
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_lr.n1_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_lr.n1_3_fold$probLeft/bst_dbf_db_tda_pca_5.40.5_lr.n1_3_fold$probRight
bst_dbf_db_tda_pca_5.40.5_lr.n1_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_lr.n1_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_lr_n1_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_lr.n1_3_fold
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.0903
## 
## $winRight
## [1] 0.9097
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_lr.n1_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_lr_n1_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_lr.n1_3_fold
## $left
## [1] 0.006402891
## 
## $rope
## [1] 0.06988669
## 
## $right
## [1] 0.9237104
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.40.5_lr_n1_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_lr.n1_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_lr_n1_3_fold))
#bf_tda_pca_5.40.5_lr.n1_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.40.5_lr_n1_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_pca_5.40.5_lr_n1_3_fold)
## t = 6.3561, df = 2, p-value = 0.02387
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  0.005476347 0.028426378
## sample estimates:
##  mean of x 
## 0.01695136
### Test set diff
diff_drybean_tda_pca_5.40.5_lr.n1_test<-(db_lr_cf_ov_acc - db_tda_pc_5.40.5_n1_db_lr_cf0_ov_acc)
diff_drybean_tda_pca_5.40.5_lr.n1_test
##  Accuracy 
## 0.3504902
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_lr.n1_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_lr.n1_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_lr.n1_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_lr.n1_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_lr.n1_test$probLeft/bst_dbf_db_tda_pca_5.40.5_lr.n1_test$probRight
bst_dbf_db_tda_pca_5.40.5_lr.n1_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_lr.n1_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_lr.n1_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_lr.n1_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1616333
## 
## $winRight
## [1] 0.8383667
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_lr.n1_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_lr.n1_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_lr.n1_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_lr.n1_test)))

#BayesFactor
#bf_tda_pca_5.40.5_lr.n1_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_lr.n1_test)) #bf_tda_pca_5.40.5_lr.n1_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_lr.n1_test))

##With TDA PCA filter 5 intervals, 50% overlap, 5 bins 
##Node2

DryBean_TDA_PC_5.40.5_n2_LrFit0 <- train(as.factor(Class) ~ ., 
                 data = tda.m_dry_bean_dataset_5.40.5.n2.vec, 
                      family = 'binomial',
                            method = 'multinom', 
                      trControl = fitControl,
                            metric='Accuracy')
## # weights:  108 (85 variable)
## initial  value 9584.121401 
## iter  10 value 5499.655931
## iter  20 value 4458.162743
## iter  30 value 3105.827306
## iter  40 value 1657.685974
## iter  50 value 1609.806849
## iter  60 value 1571.065970
## iter  70 value 1553.127350
## iter  80 value 1549.271649
## iter  90 value 1546.388947
## iter 100 value 1544.154888
## final  value 1544.154888 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 9584.121401 
## iter  10 value 5499.656416
## iter  20 value 4458.167602
## iter  30 value 3330.846257
## iter  40 value 1790.971429
## iter  50 value 1705.215957
## iter  60 value 1682.876917
## iter  70 value 1678.731763
## iter  80 value 1678.302318
## iter  90 value 1678.233365
## iter 100 value 1678.221398
## final  value 1678.221398 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 9584.121401 
## iter  10 value 5499.655932
## iter  20 value 4458.162772
## iter  30 value 3106.056700
## iter  40 value 1658.354105
## iter  50 value 1611.301945
## iter  60 value 1575.882214
## iter  70 value 1561.009908
## iter  80 value 1557.989667
## iter  90 value 1555.895117
## iter 100 value 1554.409435
## final  value 1554.409435 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 9584.121401 
## iter  10 value 5588.881570
## iter  20 value 4328.962915
## iter  30 value 2920.240822
## iter  40 value 1672.571403
## iter  50 value 1611.857568
## iter  60 value 1574.108362
## iter  70 value 1541.104264
## iter  80 value 1534.941738
## iter  90 value 1532.408660
## iter 100 value 1530.145851
## final  value 1530.145851 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 9584.121401 
## iter  10 value 5588.881911
## iter  20 value 4328.972491
## iter  30 value 2990.068226
## iter  40 value 1804.489492
## iter  50 value 1701.904225
## iter  60 value 1681.897094
## iter  70 value 1674.158988
## iter  80 value 1673.837777
## iter  90 value 1673.803635
## iter 100 value 1673.800106
## final  value 1673.800106 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 9584.121401 
## iter  10 value 5588.881570
## iter  20 value 4328.962837
## iter  30 value 2920.266218
## iter  40 value 1673.176812
## iter  50 value 1613.256935
## iter  60 value 1578.140430
## iter  70 value 1549.759917
## iter  80 value 1544.827658
## iter  90 value 1542.839816
## iter 100 value 1541.028040
## final  value 1541.028040 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 9585.913160 
## iter  10 value 5642.301786
## iter  20 value 4516.220244
## iter  30 value 2994.521146
## iter  40 value 1675.118344
## iter  50 value 1622.410746
## iter  60 value 1595.366834
## iter  70 value 1566.414674
## iter  80 value 1561.314251
## iter  90 value 1557.511262
## iter 100 value 1554.571364
## final  value 1554.571364 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 9585.913160 
## iter  10 value 5642.302128
## iter  20 value 4516.225948
## iter  30 value 3048.877381
## iter  40 value 1829.963151
## iter  50 value 1720.696625
## iter  60 value 1699.721184
## iter  70 value 1692.898520
## iter  80 value 1692.550231
## iter  90 value 1692.532587
## iter 100 value 1692.529835
## final  value 1692.529835 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 9585.913160 
## iter  10 value 5642.301787
## iter  20 value 4516.220247
## iter  30 value 2994.573763
## iter  40 value 1675.847582
## iter  50 value 1623.852678
## iter  60 value 1598.692713
## iter  70 value 1573.768811
## iter  80 value 1569.736621
## iter  90 value 1566.549705
## iter 100 value 1564.358332
## final  value 1564.358332 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 14377.077981 
## iter  10 value 7983.921257
## iter  20 value 5824.210106
## iter  30 value 4819.144952
## iter  40 value 2498.278848
## iter  50 value 2423.505014
## iter  60 value 2368.194615
## iter  70 value 2346.643831
## iter  80 value 2341.303225
## iter  90 value 2338.050676
## iter 100 value 2332.160306
## final  value 2332.160306 
## stopped after 100 iterations
DryBean_TDA_PC_5.40.5_n2_LrFit0
## Penalized Multinomial Regression 
## 
## 8024 samples
##   16 predictor
##    6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 5349, 5349, 5350 
## Resampling results across tuning parameters:
## 
##   decay  Accuracy   Kappa    
##   0e+00  0.8884594  0.8544936
##   1e-04  0.8882100  0.8541548
##   1e-01  0.8852194  0.8502247
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was decay = 0.
DryBean_TDA_PC_5.40.5_n2_LrFit0$resample
##    Accuracy     Kappa Resample
## 1 0.8863126 0.8519016    Fold3
## 2 0.8844860 0.8489939    Fold2
## 3 0.8945794 0.8625852    Fold1
db_tda_pc_5.40.5_n2_lr_fit_re<-DryBean_TDA_PC_5.40.5_n2_LrFit0$resample[1]

summary(DryBean_TDA_PC_5.40.5_n2_LrFit0)
## Call:
## nnet::multinom(formula = .outcome ~ ., data = dat, decay = param$decay, 
##     family = "binomial")
## 
## Coefficients:
##          (Intercept)        Area  Perimeter MajorAxisLength MinorAxisLength
## CALI       19.409745 0.003250476 -0.1843568       2.1409408       2.6310521
## DERMASON   -5.698789 0.004877043  0.1930933       0.8704822       1.6323456
## HOROZ      12.877483 0.008837950  0.1007845       2.8286058       4.1033595
## SEKER     -15.458689 0.005734884  0.2147872       0.2995945      -0.4809507
## SIRA       55.845899 0.003984098 -0.4003069       2.4552142       2.7973113
##          AspectRation Eccentricity   ConvexArea EquivDiameter     Extent
## CALI       -68.514521     81.17577 -0.004126732    -3.7448698   3.730739
## DERMASON    -6.698071     46.58725 -0.006210418    -2.9169298 -16.563219
## HOROZ      -46.726333     97.64395 -0.007863913    -7.7231540  -7.808393
## SEKER      -45.132989    -25.62675 -0.006622925    -0.4265182 -12.104241
## SIRA       -85.227129    122.09640 -0.005791664    -3.4567569  -8.074913
##           Solidity  roundness Compactness ShapeFactor1 ShapeFactor2
## CALI      19.97221  -48.32506    3.181868   0.41119286   0.06073577
## DERMASON -31.38751  129.63293  -17.748612   0.01000724  -0.27067390
## HOROZ     51.81586   73.28848  -11.521348  -0.09262879  -0.25068627
## SEKER    -40.32917  123.09272   12.455957  -0.50130500   0.26665353
## SIRA      46.31845 -156.27956   36.727266   1.06032004   0.24494081
##          ShapeFactor3 ShapeFactor4
## CALI      -18.2862050    -8.868924
## DERMASON  -33.1093020   -20.501120
## HOROZ     -41.5232264     4.092994
## SEKER      37.1268348   -10.678281
## SIRA       -0.7282607    18.820626
## 
## Std. Errors:
##           (Intercept)         Area   Perimeter MajorAxisLength MinorAxisLength
## CALI     3.153291e-06 0.0004296838 0.001515468    0.0007345180    0.0004715310
## DERMASON 8.218137e-06 0.0007808195 0.002708497    0.0015229222    0.0018909087
## HOROZ    4.723093e-06 0.0007228365 0.002114824    0.0008565766    0.0005375649
## SEKER    5.080934e-06 0.0007449363 0.002414981    0.0007347658    0.0005852958
## SIRA     6.090966e-06 0.0006261116 0.002260123    0.0022039847    0.0022181577
##          AspectRation Eccentricity   ConvexArea EquivDiameter       Extent
## CALI     6.342994e-06 2.622437e-06 0.0004307586  0.0004484459 2.449513e-06
## DERMASON 1.699380e-05 6.169324e-06 0.0007981367  0.0009024962 8.064631e-06
## HOROZ    8.314309e-06 3.801509e-06 0.0007278660  0.0006423939 3.191821e-06
## SEKER    6.778620e-06 3.557745e-06 0.0007546640  0.0006555003 3.960020e-06
## SIRA     2.329522e-05 7.676767e-06 0.0006359710  0.0007593239 6.691062e-06
##              Solidity    roundness  Compactness ShapeFactor1 ShapeFactor2
## CALI     3.121039e-06 2.835941e-06 2.821441e-06 2.689247e-08 9.038095e-09
## DERMASON 8.120558e-06 8.904618e-06 1.152242e-05 6.652709e-08 5.940042e-08
## HOROZ    4.672319e-06 4.237841e-06 3.761210e-06 4.320281e-08 1.053479e-08
## SEKER    5.022807e-06 4.199672e-06 4.393347e-06 4.424373e-08 1.458596e-08
## SIRA     5.988616e-06 8.456910e-06 1.173750e-05 3.755328e-08 6.305819e-08
##          ShapeFactor3 ShapeFactor4
## CALI     2.713365e-06 3.164450e-06
## DERMASON 1.380002e-05 8.236162e-06
## HOROZ    3.077089e-06 4.718149e-06
## SEKER    3.790540e-06 5.078708e-06
## SIRA     1.533446e-05 6.196997e-06
## 
## Residual Deviance: 4664.321 
## AIC: 4834.321
vip(DryBean_TDA_PC_5.40.5_n2_LrFit0,50) + ggtitle("dryBean_TDA_PCA_5.40.5_n2_Lr1Fit TDA-Assited LR")

# Predict outcome using DryBean_TDA_PC_5.40.5_n2_LrFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.40.5_n2_LrFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.40.5_n2_db_lr_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length

## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
db_tda_pc_5.40.5_n2_db_lr_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      354    122   15        1     1     3    4
##   BOMBAY          0      0    0        0     0     0    0
##   CALI           28      0  446        0     8     0    1
##   DERMASON        0      0    0      985     3    18   68
##   HOROZ           2     34   21        3   558     1    8
##   SEKER           3      0    1        9     0   566   13
##   SIRA            9      0    6       65     8    20  696
## 
## Overall Statistics
##                                           
##                Accuracy : 0.8836          
##                  95% CI : (0.8733, 0.8933)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.8587          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.89394       0.00000      0.9121          0.9266
## Specificity                  0.96037       1.00000      0.9897          0.9705
## Pos Pred Value               0.70800           NaN      0.9234          0.9171
## Neg Pred Value               0.98827       0.96176      0.9880          0.9741
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.08676       0.00000      0.1093          0.2414
## Detection Prevalence         0.12255       0.00000      0.1184          0.2632
## Balanced Accuracy            0.92715       0.50000      0.9509          0.9486
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9654       0.9309      0.8810
## Specificity                0.9803       0.9925      0.9672
## Pos Pred Value             0.8900       0.9561      0.8657
## Neg Pred Value             0.9942       0.9880      0.9713
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1368       0.1387      0.1706
## Detection Prevalence       0.1537       0.1451      0.1971
## Balanced Accuracy          0.9728       0.9617      0.9241
db_tda_pc_5.40.5_n2_db_lr_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      354    122   15        1     1     3    4
##   BOMBAY          0      0    0        0     0     0    0
##   CALI           28      0  446        0     8     0    1
##   DERMASON        0      0    0      985     3    18   68
##   HOROZ           2     34   21        3   558     1    8
##   SEKER           3      0    1        9     0   566   13
##   SIRA            9      0    6       65     8    20  696
## 
## Overall Statistics
##                                           
##                Accuracy : 0.8836          
##                  95% CI : (0.8733, 0.8933)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.8587          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.89394       0.00000      0.9121          0.9266
## Specificity                  0.96037       1.00000      0.9897          0.9705
## Pos Pred Value               0.70800           NaN      0.9234          0.9171
## Neg Pred Value               0.98827       0.96176      0.9880          0.9741
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.08676       0.00000      0.1093          0.2414
## Detection Prevalence         0.12255       0.00000      0.1184          0.2632
## Balanced Accuracy            0.92715       0.50000      0.9509          0.9486
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9654       0.9309      0.8810
## Specificity                0.9803       0.9925      0.9672
## Pos Pred Value             0.8900       0.9561      0.8657
## Neg Pred Value             0.9942       0.9880      0.9713
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1368       0.1387      0.1706
## Detection Prevalence       0.1537       0.1451      0.1971
## Balanced Accuracy          0.9728       0.9617      0.9241
db_tda_pc_5.40.5_n2_db_lr_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.8835784      0.8586748      0.8733388      0.8932651      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
db_tda_pc_5.40.5_n2_db_lr_cf0_ov_acc<-db_tda_pc_5.40.5_n2_db_lr_cf0$overall[1]
db_tda_pc_5.40.5_n2_db_lr_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.8939394   0.9603692      0.7080000      0.9882682 0.7080000
## Class: BOMBAY     0.0000000   1.0000000            NaN      0.9617647        NA
## Class: CALI       0.9120654   0.9896965      0.9233954      0.9880456 0.9233954
## Class: DERMASON   0.9266228   0.9705005      0.9171322      0.9740519 0.9171322
## Class: HOROZ      0.9653979   0.9802970      0.8899522      0.9942079 0.8899522
## Class: SEKER      0.9309211   0.9925115      0.9560811      0.9879587 0.9560811
## Class: SIRA       0.8810127   0.9671733      0.8656716      0.9713065 0.8656716
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.8939394 0.7901786 0.09705882     0.08676471
## Class: BOMBAY   0.0000000        NA 0.03823529     0.00000000
## Class: CALI     0.9120654 0.9176955 0.11985294     0.10931373
## Class: DERMASON 0.9266228 0.9218531 0.26053922     0.24142157
## Class: HOROZ    0.9653979 0.9261411 0.14166667     0.13676471
## Class: SEKER    0.9309211 0.9433333 0.14901961     0.13872549
## Class: SIRA     0.8810127 0.8732748 0.19362745     0.17058824
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA            0.1225490         0.9271543
## Class: BOMBAY              0.0000000         0.5000000
## Class: CALI                0.1183824         0.9508810
## Class: DERMASON            0.2632353         0.9485616
## Class: HOROZ               0.1536765         0.9728474
## Class: SEKER               0.1450980         0.9617163
## Class: SIRA                0.1970588         0.9240930
db_tda_pc_5.40.5_n2_db_lr_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n2_db_lr_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted LR vs. tda-assisted LR classifiers

### 3-fold diff

diff_drybean_tda_pca_5.40.5_lr_n2_3_fold<-(db_lr_fit_re - db_tda_pc_5.40.5_n2_lr_fit_re)
diff_drybean_tda_pca_5.40.5_lr_n2_3_fold
##     Accuracy
## 1 0.04473270
## 2 0.03965629
## 3 0.02895738
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_lr.n2_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_lr_n2_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_lr.n2_3_fold
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_lr.n2_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_lr.n2_3_fold$probLeft/bst_dbf_db_tda_pca_5.40.5_lr.n2_3_fold$probRight
bst_dbf_db_tda_pca_5.40.5_lr.n2_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_lr.n2_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_lr_n2_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_lr.n2_3_fold
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.0096
## 
## $winRight
## [1] 0.9904
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_lr.n2_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_lr_n2_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_lr.n2_3_fold
## $left
## [1] 0.006194892
## 
## $rope
## [1] 0.01149125
## 
## $right
## [1] 0.9823139
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.40.5_lr_n2_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_lr.n2_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_lr_n2_3_fold))
#bf_tda_pca_5.40.5_lr.n2_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.40.5_lr_n2_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_pca_5.40.5_lr_n2_3_fold)
## t = 8.1263, df = 2, p-value = 0.01481
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  0.01777755 0.05778670
## sample estimates:
##  mean of x 
## 0.03778212
### Test set diff
diff_drybean_tda_pca_5.40.5_lr.n2_test<-(db_lr_cf_ov_acc - db_tda_pc_5.40.5_n2_db_lr_cf0_ov_acc)
diff_drybean_tda_pca_5.40.5_lr.n2_test
##   Accuracy 
## 0.04093137
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_lr.n2_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_lr.n2_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_lr.n2_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_lr.n2_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_lr.n2_test$probLeft/bst_dbf_db_tda_pca_5.40.5_lr.n2_test$probRight
bst_dbf_db_tda_pca_5.40.5_lr.n2_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_lr.n2_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_lr.n2_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_lr.n2_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1587
## 
## $winRight
## [1] 0.8413
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_lr.n2_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_lr.n2_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_lr.n2_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_lr.n2_test)))

#BayesFactor
#bf_tda_pca_5.40.5_lr.n2_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_lr.n2_test)) #bf_tda_pca_5.40.5_lr.n2_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_lr.n2_test))

##Node3

DryBean_TDA_PC_5.40.5_n3_LrFit0 <- train(as.factor(Class) ~ ., 
                 data = tda.m_dry_bean_dataset_5.40.5.n3.vec, 
                      family = 'binomial',
                            method = 'multinom', 
                      trControl = fitControl,
                            metric='Accuracy')
## Warning in nnet::multinom(.outcome ~ ., data = dat, decay = param$decay, :
## group 'DERMASON' is empty
## # weights:  108 (85 variable)
## initial  value 5979.101349 
## iter  10 value 3034.372966
## iter  20 value 1972.910199
## iter  30 value 1137.199499
## iter  40 value 540.778137
## iter  50 value 521.497566
## iter  60 value 513.684150
## iter  70 value 510.646121
## iter  80 value 507.225480
## iter  90 value 503.932490
## iter 100 value 502.901038
## final  value 502.901038 
## stopped after 100 iterations
## Warning in nnet::multinom(.outcome ~ ., data = dat, decay = param$decay, :
## group 'DERMASON' is empty
## # weights:  108 (85 variable)
## initial  value 5979.101349 
## iter  10 value 3034.373056
## iter  20 value 1972.927169
## iter  30 value 1207.735281
## iter  40 value 614.025645
## iter  50 value 564.297128
## iter  60 value 556.306725
## iter  70 value 553.667913
## iter  80 value 552.870496
## iter  90 value 551.434461
## iter 100 value 550.618992
## final  value 550.618992 
## stopped after 100 iterations
## Warning in nnet::multinom(.outcome ~ ., data = dat, decay = param$decay, :
## group 'DERMASON' is empty
## # weights:  108 (85 variable)
## initial  value 5979.101349 
## iter  10 value 3034.372965
## iter  20 value 1972.910191
## iter  30 value 1137.252810
## iter  40 value 541.199981
## iter  50 value 522.170452
## iter  60 value 514.837178
## iter  70 value 512.187467
## iter  80 value 509.640395
## iter  90 value 507.712387
## iter 100 value 507.219068
## final  value 507.219068 
## stopped after 100 iterations
## # weights:  126 (102 variable)
## initial  value 6497.393988 
## iter  10 value 3611.367398
## iter  20 value 2782.757750
## iter  30 value 2392.307299
## iter  40 value 805.226361
## iter  50 value 522.418935
## iter  60 value 503.743512
## iter  70 value 495.963211
## iter  80 value 487.471254
## iter  90 value 480.571957
## iter 100 value 476.988806
## final  value 476.988806 
## stopped after 100 iterations
## # weights:  126 (102 variable)
## initial  value 6497.393988 
## iter  10 value 3611.367411
## iter  20 value 2782.761229
## iter  30 value 2392.430760
## iter  40 value 816.341105
## iter  50 value 597.278271
## iter  60 value 551.924881
## iter  70 value 538.296627
## iter  80 value 535.537853
## iter  90 value 535.129046
## iter 100 value 534.478572
## final  value 534.478572 
## stopped after 100 iterations
## # weights:  126 (102 variable)
## initial  value 6497.393988 
## iter  10 value 3611.367396
## iter  20 value 2782.757398
## iter  30 value 2392.215448
## iter  40 value 824.403681
## iter  50 value 520.222817
## iter  60 value 504.585704
## iter  70 value 497.370053
## iter  80 value 490.728625
## iter  90 value 485.308346
## iter 100 value 483.146108
## final  value 483.146108 
## stopped after 100 iterations
## Warning in nnet::multinom(.outcome ~ ., data = dat, decay = param$decay, :
## group 'SEKER' is empty
## # weights:  108 (85 variable)
## initial  value 5984.476627 
## iter  10 value 3107.003016
## iter  20 value 2022.401459
## iter  30 value 1098.756154
## iter  40 value 546.565060
## iter  50 value 516.336049
## iter  60 value 505.718495
## iter  70 value 499.593011
## iter  80 value 489.790266
## iter  90 value 485.999525
## iter 100 value 482.232741
## final  value 482.232741 
## stopped after 100 iterations
## Warning in nnet::multinom(.outcome ~ ., data = dat, decay = param$decay, :
## group 'SEKER' is empty
## # weights:  108 (85 variable)
## initial  value 5984.476627 
## iter  10 value 3107.003101
## iter  20 value 2022.409974
## iter  30 value 1149.637349
## iter  40 value 641.109887
## iter  50 value 566.916922
## iter  60 value 557.488085
## iter  70 value 554.953318
## iter  80 value 554.392297
## iter  90 value 553.571533
## iter 100 value 553.159426
## final  value 553.159426 
## stopped after 100 iterations
## Warning in nnet::multinom(.outcome ~ ., data = dat, decay = param$decay, :
## group 'SEKER' is empty
## # weights:  108 (85 variable)
## initial  value 5984.476627 
## iter  10 value 3107.003017
## iter  20 value 2022.401502
## iter  30 value 1098.837475
## iter  40 value 546.954658
## iter  50 value 517.012883
## iter  60 value 507.127067
## iter  70 value 501.788626
## iter  80 value 495.029579
## iter  90 value 492.796009
## iter 100 value 490.933113
## final  value 490.933113 
## stopped after 100 iterations
## # weights:  126 (102 variable)
## initial  value 9745.118026 
## iter  10 value 4517.266615
## iter  20 value 3324.528646
## iter  30 value 3068.370504
## iter  40 value 1210.781359
## iter  50 value 782.151109
## iter  60 value 766.188183
## iter  70 value 754.909120
## iter  80 value 744.747552
## iter  90 value 739.282538
## iter 100 value 736.298164
## final  value 736.298164 
## stopped after 100 iterations
DryBean_TDA_PC_5.40.5_n3_LrFit0
## Penalized Multinomial Regression 
## 
## 5008 samples
##   16 predictor
##    7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 3337, 3339, 3340 
## Resampling results across tuning parameters:
## 
##   decay  Accuracy   Kappa    
##   0e+00  0.9452847  0.9207964
##   1e-04  0.9448856  0.9202176
##   1e-01  0.9410931  0.9145963
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was decay = 0.
DryBean_TDA_PC_5.40.5_n3_LrFit0$resample
##    Accuracy     Kappa Resample
## 1 0.9436451 0.9185310    Fold3
## 2 0.9406830 0.9141842    Fold2
## 3 0.9515260 0.9296741    Fold1
db_tda_pc_5.40.5_n3_lr_fit_re<-DryBean_TDA_PC_5.40.5_n2_LrFit0$resample[1]

summary(DryBean_TDA_PC_5.40.5_n3_LrFit0)
## Call:
## nnet::multinom(formula = .outcome ~ ., data = dat, decay = param$decay, 
##     family = "binomial")
## 
## Coefficients:
##          (Intercept)         Area    Perimeter MajorAxisLength MinorAxisLength
## BOMBAY      14.30163 0.0119320472 -0.044989215        1.400681        2.722384
## CALI        22.66676 0.0029400067 -0.173611175        1.834276        2.695622
## DERMASON    18.91713 0.0051320514  0.008693355        1.488015        2.368055
## HOROZ      -14.57903 0.0067383099  0.100545807        1.798648        4.106024
## SEKER       22.37020 0.0031423125 -0.053428700        2.049694        3.378469
## SIRA        19.27074 0.0004244842 -0.015470968        2.206961        1.498233
##          AspectRation Eccentricity   ConvexArea EquivDiameter    Extent
## BOMBAY       39.57132     16.73072 -0.009883308     -4.703733  1.761246
## CALI        -64.63282    134.18415 -0.003374680     -3.631153  4.815713
## DERMASON     12.04918     14.16334 -0.003573557     -4.595545 10.907022
## HOROZ        26.72792     88.26114 -0.006462026     -6.056983 -2.812408
## SEKER        27.61690     10.69756 -0.001150640     -6.109034  7.170042
## SIRA       -127.59671     53.41189 -0.003939836     -2.752798 -7.929204
##           Solidity  roundness Compactness ShapeFactor1 ShapeFactor2
## BOMBAY   11.671991   1.787331    8.556089   0.23812164   0.02412847
## CALI     36.024767 -41.130548  -17.816503   0.40408364  -0.26340126
## DERMASON 15.003534   8.381469   17.315752   0.29755257   0.11808973
## HOROZ     6.304111  77.129026  -57.951286   0.12273240  -0.54395322
## SEKER    13.257169   5.877069   20.392897   0.38131795   0.13038006
## SIRA      8.333177  53.902684    5.922615  -0.01003625  -0.04082324
##          ShapeFactor3 ShapeFactor4
## BOMBAY       4.889735     13.88936
## CALI       -61.811757    -14.36913
## DERMASON    14.681975     16.24844
## HOROZ      -98.532192    -26.39504
## SEKER       18.235966     19.70641
## SIRA       -17.035093    -16.15048
## 
## Std. Errors:
##           (Intercept)         Area    Perimeter MajorAxisLength MinorAxisLength
## BOMBAY   1.040351e-07 1.526589e-04 5.729836e-05    1.997657e-05    2.445222e-05
## CALI     9.353647e-06 3.104293e-04 1.151684e-03    3.464698e-03    4.101794e-03
## DERMASON 7.810083e-09 6.357547e-05 2.695823e-06    2.053323e-06    4.939265e-07
## HOROZ    4.841449e-06 3.935087e-04 2.063908e-03    2.305130e-03    2.004134e-03
## SEKER    6.915675e-08 5.654433e-05 2.946852e-05    1.084834e-05    6.805963e-06
## SIRA     7.083522e-06 5.782615e-04 3.350225e-03    1.136800e-03    7.153371e-04
##          AspectRation Eccentricity   ConvexArea EquivDiameter       Extent
## BOMBAY   1.547259e-07 6.412178e-08 1.509108e-04  1.603800e-05 1.033238e-07
## CALI     3.124845e-05 4.607702e-06 3.088814e-04  1.383446e-03 8.806939e-06
## DERMASON 2.155067e-08 8.935071e-09 6.568000e-05  1.042589e-06 4.767306e-09
## HOROZ    2.126979e-05 4.342506e-06 3.908684e-04  6.999337e-04 3.884852e-06
## SEKER    1.154908e-07 5.640751e-08 5.918948e-05  8.679923e-06 4.479719e-08
## SIRA     1.188234e-05 5.716070e-06 5.857954e-04  9.071344e-04 4.835332e-06
##              Solidity    roundness  Compactness ShapeFactor1 ShapeFactor2
## BOMBAY   1.009756e-07 8.387732e-08 1.108370e-07 7.470359e-10 3.315867e-10
## CALI     9.212213e-06 1.508136e-05 1.727763e-05 1.813995e-08 6.443281e-08
## DERMASON 7.967422e-09 1.202887e-08 4.254419e-09 9.520755e-11 8.958808e-12
## HOROZ    4.764342e-06 6.536049e-06 8.412030e-06 3.295465e-08 3.207956e-08
## SEKER    6.849828e-08 5.730269e-08 5.315356e-08 7.057375e-10 1.448740e-10
## SIRA     6.977021e-06 5.474149e-06 5.478494e-06 6.923424e-08 1.462953e-08
##          ShapeFactor3 ShapeFactor4
## BOMBAY   1.170527e-07 1.040068e-07
## CALI     2.097073e-05 9.152293e-06
## DERMASON 2.462094e-09 7.750721e-09
## HOROZ    1.030359e-05 4.739947e-06
## SEKER    4.067398e-08 6.903252e-08
## SIRA     4.239731e-06 7.105550e-06
## 
## Residual Deviance: 1472.596 
## AIC: 1676.596
vip(DryBean_TDA_PC_5.40.5_n3_LrFit0,50) + ggtitle("dryBean_TDA_PCA_5.40.5_n3_Lr1Fit TDA-Assited LR")

# Predict outcome using DryBean_TDA_PC_5.40.5_n3_LrFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.40.5_n3_LrFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.40.5_n3_db_lr_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
db_tda_pc_5.40.5_n3_db_lr_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      358      0   14        2     2    35    9
##   BOMBAY          0    147    0        1     0    14    0
##   CALI           29      0  462        0     8     0    3
##   DERMASON        0      0    0       13     0     2    1
##   HOROZ           2      0    9       19   561     0   14
##   SEKER           1      9    0      220     0   187    0
##   SIRA            6      0    4      808     7   370  763
## 
## Overall Statistics
##                                           
##                Accuracy : 0.6105          
##                  95% CI : (0.5954, 0.6255)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.5383          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.90404       0.94231      0.9448        0.012230
## Specificity                  0.98317       0.99618      0.9889        0.999006
## Pos Pred Value               0.85238       0.90741      0.9203        0.812500
## Neg Pred Value               0.98962       0.99770      0.9925        0.741634
## Prevalence                   0.09706       0.03824      0.1199        0.260539
## Detection Rate               0.08775       0.03603      0.1132        0.003186
## Detection Prevalence         0.10294       0.03971      0.1230        0.003922
## Balanced Accuracy            0.94361       0.96924      0.9668        0.505618
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9706      0.30757      0.9658
## Specificity                0.9874      0.93376      0.6368
## Pos Pred Value             0.9273      0.44844      0.3897
## Neg Pred Value             0.9951      0.88507      0.9873
## Prevalence                 0.1417      0.14902      0.1936
## Detection Rate             0.1375      0.04583      0.1870
## Detection Prevalence       0.1483      0.10221      0.4799
## Balanced Accuracy          0.9790      0.62066      0.8013
db_tda_pc_5.40.5_n3_db_lr_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      358      0   14        2     2    35    9
##   BOMBAY          0    147    0        1     0    14    0
##   CALI           29      0  462        0     8     0    3
##   DERMASON        0      0    0       13     0     2    1
##   HOROZ           2      0    9       19   561     0   14
##   SEKER           1      9    0      220     0   187    0
##   SIRA            6      0    4      808     7   370  763
## 
## Overall Statistics
##                                           
##                Accuracy : 0.6105          
##                  95% CI : (0.5954, 0.6255)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.5383          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.90404       0.94231      0.9448        0.012230
## Specificity                  0.98317       0.99618      0.9889        0.999006
## Pos Pred Value               0.85238       0.90741      0.9203        0.812500
## Neg Pred Value               0.98962       0.99770      0.9925        0.741634
## Prevalence                   0.09706       0.03824      0.1199        0.260539
## Detection Rate               0.08775       0.03603      0.1132        0.003186
## Detection Prevalence         0.10294       0.03971      0.1230        0.003922
## Balanced Accuracy            0.94361       0.96924      0.9668        0.505618
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9706      0.30757      0.9658
## Specificity                0.9874      0.93376      0.6368
## Pos Pred Value             0.9273      0.44844      0.3897
## Neg Pred Value             0.9951      0.88507      0.9873
## Prevalence                 0.1417      0.14902      0.1936
## Detection Rate             0.1375      0.04583      0.1870
## Detection Prevalence       0.1483      0.10221      0.4799
## Balanced Accuracy          0.9790      0.62066      0.8013
db_tda_pc_5.40.5_n3_db_lr_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.6105392      0.5383142      0.5953792      0.6255407      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
db_tda_pc_5.40.5_n3_db_lr_cf0_ov_acc<-db_tda_pc_5.40.5_n3_db_lr_cf0$overall[1]
db_tda_pc_5.40.5_n3_db_lr_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA  0.90404040   0.9831705      0.8523810      0.9896175 0.8523810
## Class: BOMBAY    0.94230769   0.9961774      0.9074074      0.9977029 0.9074074
## Class: CALI      0.94478528   0.9888610      0.9203187      0.9924539 0.9203187
## Class: DERMASON  0.01222954   0.9990056      0.8125000      0.7416339 0.8125000
## Class: HOROZ     0.97058824   0.9874358      0.9272727      0.9951079 0.9272727
## Class: SEKER     0.30756579   0.9337558      0.4484412      0.8850669 0.4484412
## Class: SIRA      0.96582278   0.6367781      0.3896834      0.9872762 0.3896834
##                     Recall         F1 Prevalence Detection Rate
## Class: BARBUNYA 0.90404040 0.87745098 0.09705882    0.087745098
## Class: BOMBAY   0.94230769 0.92452830 0.03823529    0.036029412
## Class: CALI     0.94478528 0.93239152 0.11985294    0.113235294
## Class: DERMASON 0.01222954 0.02409639 0.26053922    0.003186275
## Class: HOROZ    0.97058824 0.94843618 0.14166667    0.137500000
## Class: SEKER    0.30756579 0.36487805 0.14901961    0.045833333
## Class: SIRA     0.96582278 0.55531295 0.19362745    0.187009804
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA          0.102941176         0.9436054
## Class: BOMBAY            0.039705882         0.9692425
## Class: CALI              0.123039216         0.9668232
## Class: DERMASON          0.003921569         0.5056176
## Class: HOROZ             0.148284314         0.9790120
## Class: SEKER             0.102205882         0.6206608
## Class: SIRA              0.479901961         0.8013005
db_tda_pc_5.40.5_n3_db_lr_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n3_db_lr_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted LR vs. tda-assisted LR classifiers

### 3-fold diff

diff_drybean_tda_pca_5.40.5_lr_n3_3_fold<-(db_lr_fit_re - db_tda_pc_5.40.5_n3_lr_fit_re)
diff_drybean_tda_pca_5.40.5_lr_n3_3_fold
##     Accuracy
## 1 0.04473270
## 2 0.03965629
## 3 0.02895738
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_lr.n3_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_lr_n3_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_lr.n3_3_fold
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_lr.n3_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_lr.n3_3_fold$probLeft/bst_dbf_db_tda_pca_5.40.5_lr.n3_3_fold$probRight
bst_dbf_db_tda_pca_5.40.5_lr.n3_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_lr.n3_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_lr_n3_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_lr.n3_3_fold
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.009
## 
## $winRight
## [1] 0.991
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_lr.n3_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_lr_n3_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_lr.n3_3_fold
## $left
## [1] 0.006194892
## 
## $rope
## [1] 0.01149125
## 
## $right
## [1] 0.9823139
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.40.5_lr_n3_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_lr.n3_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_lr_n3_3_fold))
#bf_tda_pca_5.40.5_lr.n3_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.40.5_lr_n3_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_pca_5.40.5_lr_n3_3_fold)
## t = 8.1263, df = 2, p-value = 0.01481
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  0.01777755 0.05778670
## sample estimates:
##  mean of x 
## 0.03778212
### Test set diff
diff_drybean_tda_pca_5.40.5_lr.n3_test<-(db_lr_cf_ov_acc - db_tda_pc_5.40.5_n3_db_lr_cf0_ov_acc)
diff_drybean_tda_pca_5.40.5_lr.n3_test
##  Accuracy 
## 0.3139706
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_lr.n3_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_lr.n3_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_lr.n3_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_lr.n3_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_lr.n3_test$probLeft/bst_dbf_db_tda_pca_5.40.5_lr.n3_test$probRight
bst_dbf_db_tda_pca_5.40.5_lr.n3_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_lr.n3_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_lr.n3_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_lr.n3_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1603
## 
## $winRight
## [1] 0.8397
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_lr.n3_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_lr.n3_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_lr.n3_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_lr.n3_test)))

#BayesFactor
#bf_tda_pca_5.40.5_lr.n3_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_lr.n3_test)) #bf_tda_pca_5.40.5_lr.n3_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_lr.n3_test))

##Node4

DryBean_TDA_PC_5.40.5_n4_LrFit0 <- train(as.factor(Class) ~ ., 
                 data = tda.m_dry_bean_dataset_5.40.5.n4.vec, 
                      family = 'binomial',
                            method = 'multinom', 
                      trControl = fitControl,
                            metric='Accuracy')
## # weights:  72 (51 variable)
## initial  value 827.617734 
## iter  10 value 287.892769
## iter  20 value 48.290919
## iter  30 value 18.197327
## iter  40 value 16.385061
## iter  50 value 15.871089
## iter  60 value 15.366131
## iter  70 value 15.067464
## iter  80 value 14.879475
## iter  90 value 14.468115
## iter 100 value 13.967846
## final  value 13.967846 
## stopped after 100 iterations
## # weights:  72 (51 variable)
## initial  value 827.617734 
## iter  10 value 287.892858
## iter  20 value 56.437727
## iter  30 value 31.400890
## iter  40 value 22.781623
## iter  50 value 21.527134
## iter  60 value 21.301004
## iter  70 value 21.242925
## iter  80 value 21.235958
## iter  90 value 21.231339
## iter 100 value 21.222071
## final  value 21.222071 
## stopped after 100 iterations
## # weights:  72 (51 variable)
## initial  value 827.617734 
## iter  10 value 287.892769
## iter  20 value 48.301381
## iter  30 value 18.387086
## iter  40 value 16.725450
## iter  50 value 16.257114
## iter  60 value 15.777313
## iter  70 value 15.552494
## iter  80 value 15.377789
## iter  90 value 15.039381
## iter 100 value 14.817205
## final  value 14.817205 
## stopped after 100 iterations
## # weights:  72 (51 variable)
## initial  value 824.845145 
## iter  10 value 294.065897
## iter  20 value 80.703610
## iter  30 value 10.253629
## iter  40 value 6.693207
## iter  50 value 3.135012
## iter  60 value 0.040042
## final  value 0.000081 
## converged
## # weights:  72 (51 variable)
## initial  value 824.845145 
## iter  10 value 294.066025
## iter  20 value 92.825732
## iter  30 value 31.325549
## iter  40 value 19.300758
## iter  50 value 14.032298
## iter  60 value 12.390638
## iter  70 value 11.418843
## iter  80 value 10.247089
## iter  90 value 10.018751
## iter 100 value 9.905763
## final  value 9.905763 
## stopped after 100 iterations
## # weights:  72 (51 variable)
## initial  value 824.845145 
## iter  10 value 294.065897
## iter  20 value 80.722985
## iter  30 value 10.614155
## iter  40 value 7.811361
## iter  50 value 6.808006
## iter  60 value 6.449186
## iter  70 value 5.928460
## iter  80 value 5.804836
## iter  90 value 5.622462
## iter 100 value 5.560178
## final  value 5.560178 
## stopped after 100 iterations
## # weights:  72 (51 variable)
## initial  value 826.231439 
## iter  10 value 291.475138
## iter  20 value 56.914065
## iter  30 value 17.798466
## iter  40 value 15.810751
## iter  50 value 14.887052
## iter  60 value 13.662806
## iter  70 value 13.225798
## iter  80 value 12.573399
## iter  90 value 12.229413
## iter 100 value 11.980528
## final  value 11.980528 
## stopped after 100 iterations
## # weights:  72 (51 variable)
## initial  value 826.231439 
## iter  10 value 291.475356
## iter  20 value 73.251467
## iter  30 value 32.577708
## iter  40 value 23.331057
## iter  50 value 21.802098
## iter  60 value 21.507033
## iter  70 value 21.409488
## iter  80 value 21.397610
## iter  90 value 21.397044
## iter 100 value 21.396031
## final  value 21.396031 
## stopped after 100 iterations
## # weights:  72 (51 variable)
## initial  value 826.231439 
## iter  10 value 291.475138
## iter  20 value 56.934233
## iter  30 value 18.043020
## iter  40 value 16.305415
## iter  50 value 15.590873
## iter  60 value 14.773262
## iter  70 value 14.480837
## iter  80 value 14.048743
## iter  90 value 13.631088
## iter 100 value 13.054438
## final  value 13.054438 
## stopped after 100 iterations
## # weights:  72 (51 variable)
## initial  value 1239.347159 
## iter  10 value 491.469166
## iter  20 value 144.769737
## iter  30 value 43.271148
## iter  40 value 35.006299
## iter  50 value 30.023970
## iter  60 value 29.283810
## iter  70 value 29.079232
## iter  80 value 28.987786
## iter  90 value 28.955490
## iter 100 value 28.928960
## final  value 28.928960 
## stopped after 100 iterations
DryBean_TDA_PC_5.40.5_n4_LrFit0
## Penalized Multinomial Regression 
## 
## 894 samples
##  16 predictor
##   4 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'HOROZ' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 597, 595, 596 
## Resampling results across tuning parameters:
## 
##   decay  Accuracy   Kappa    
##   0e+00  0.9865771  0.9782208
##   1e-04  0.9854585  0.9763911
##   1e-01  0.9865883  0.9782445
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was decay = 0.1.
DryBean_TDA_PC_5.40.5_n4_LrFit0$resample
##    Accuracy     Kappa Resample
## 1 0.9898990 0.9836367    Fold1
## 2 0.9899329 0.9837584    Fold3
## 3 0.9799331 0.9673385    Fold2
db_tda_pc_5.40.5_n4_lr_fit_re<-DryBean_TDA_PC_5.40.5_n4_LrFit0$resample[1]

summary(DryBean_TDA_PC_5.40.5_n4_LrFit0)
## Call:
## nnet::multinom(formula = .outcome ~ ., data = dat, decay = param$decay, 
##     family = "binomial")
## 
## Coefficients:
##        (Intercept)          Area   Perimeter MajorAxisLength MinorAxisLength
## BOMBAY  0.01498850  8.994831e-05 -0.17872410      0.09345407      0.25973206
## CALI   -0.09819527 -7.546277e-04 -0.13674946      0.14665909     -0.06239474
## HOROZ   0.03738493  1.642183e-03 -0.04423673      0.83466570      0.98109527
##         AspectRation Eccentricity    ConvexArea EquivDiameter      Extent
## BOMBAY -0.0002061687   0.00828700  0.0005875650    0.07700161 -0.01604308
## CALI   -0.3144317239  -0.06668309  0.0005647395    0.38747829  1.78446833
## HOROZ   0.0654230588   0.01804208 -0.0024831873   -1.48717107 -1.57359246
##           Solidity    roundness Compactness  ShapeFactor1  ShapeFactor2
## BOMBAY  0.01022276  0.006225626  0.01507791  0.0002847631  7.387258e-05
## CALI   -0.07505404 -0.051267763 -0.06489145 -0.0020663072 -2.472824e-04
## HOROZ   0.02143842  0.031875922  0.02724669  0.0007367023  1.492520e-04
##        ShapeFactor3 ShapeFactor4
## BOMBAY   0.01422970   0.01129805
## CALI    -0.05400081  -0.09580005
## HOROZ    0.02231688   0.01887835
## 
## Std. Errors:
##         (Intercept)         Area    Perimeter MajorAxisLength MinorAxisLength
## BOMBAY 3.085765e-07 0.0041975019 0.0001299719    5.886092e-05    6.695408e-05
## CALI   6.753371e-06 0.0007049863 0.0052356293    2.140798e-03    6.322020e-04
## HOROZ  1.671316e-05 0.0008825559 0.0120565408    3.734688e-03    1.668966e-03
##        AspectRation Eccentricity   ConvexArea EquivDiameter       Extent
## BOMBAY 3.942434e-07 1.929994e-07 0.0041263391  6.798267e-05 2.436854e-07
## CALI   1.612936e-05 6.467529e-06 0.0007192361  1.118265e-03 4.179480e-06
## HOROZ  3.421790e-05 1.452213e-05 0.0009620845  2.469209e-03 9.954239e-06
##            Solidity    roundness  Compactness ShapeFactor1 ShapeFactor2
## BOMBAY 3.594836e-07 4.002417e-07 3.051704e-07 1.534208e-09 6.803567e-10
## CALI   6.600131e-06 4.390336e-06 4.364825e-06 6.014808e-08 8.807479e-09
## HOROZ  1.622201e-05 8.710748e-06 1.157344e-05 1.539993e-07 2.336175e-08
##        ShapeFactor3 ShapeFactor4
## BOMBAY 2.952891e-07 3.482553e-07
## CALI   2.973951e-06 6.635355e-06
## HOROZ  8.020001e-06 1.633802e-05
## 
## Residual Deviance: 57.85792 
## AIC: 159.8579
vip(DryBean_TDA_PC_5.40.5_n4_LrFit0,50) + ggtitle("dryBean_TDA_PCA_5.40.5_n4_Lr1Fit TDA-Assited LR")

# Predict outcome using DryBean_TDA_PC_5.40.5_n4_LrFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.40.5_n4_LrFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.40.5_n4_db_lr_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length

## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
db_tda_pc_5.40.5_n4_db_lr_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      201      0    1        0     1     9    0
##   BOMBAY          0    156    0        0     0     0    0
##   CALI          180      0  479       53    13   226  437
##   DERMASON        0      0    0        0     0     0    0
##   HOROZ          15      0    9     1010   564   373  353
##   SEKER           0      0    0        0     0     0    0
##   SIRA            0      0    0        0     0     0    0
## 
## Overall Statistics
##                                           
##                Accuracy : 0.3431          
##                  95% CI : (0.3286, 0.3579)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.2467          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.50758       1.00000      0.9796          0.0000
## Specificity                  0.99701       1.00000      0.7469          1.0000
## Pos Pred Value               0.94811       1.00000      0.3451             NaN
## Neg Pred Value               0.94959       1.00000      0.9963          0.7395
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.04926       0.03824      0.1174          0.0000
## Detection Prevalence         0.05196       0.03824      0.3402          0.0000
## Balanced Accuracy            0.75229       1.00000      0.8632          0.5000
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9758        0.000      0.0000
## Specificity                0.4974        1.000      1.0000
## Pos Pred Value             0.2427          NaN         NaN
## Neg Pred Value             0.9920        0.851      0.8064
## Prevalence                 0.1417        0.149      0.1936
## Detection Rate             0.1382        0.000      0.0000
## Detection Prevalence       0.5696        0.000      0.0000
## Balanced Accuracy          0.7366        0.500      0.5000
db_tda_pc_5.40.5_n4_db_lr_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      201      0    1        0     1     9    0
##   BOMBAY          0    156    0        0     0     0    0
##   CALI          180      0  479       53    13   226  437
##   DERMASON        0      0    0        0     0     0    0
##   HOROZ          15      0    9     1010   564   373  353
##   SEKER           0      0    0        0     0     0    0
##   SIRA            0      0    0        0     0     0    0
## 
## Overall Statistics
##                                           
##                Accuracy : 0.3431          
##                  95% CI : (0.3286, 0.3579)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.2467          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.50758       1.00000      0.9796          0.0000
## Specificity                  0.99701       1.00000      0.7469          1.0000
## Pos Pred Value               0.94811       1.00000      0.3451             NaN
## Neg Pred Value               0.94959       1.00000      0.9963          0.7395
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.04926       0.03824      0.1174          0.0000
## Detection Prevalence         0.05196       0.03824      0.3402          0.0000
## Balanced Accuracy            0.75229       1.00000      0.8632          0.5000
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9758        0.000      0.0000
## Specificity                0.4974        1.000      1.0000
## Pos Pred Value             0.2427          NaN         NaN
## Neg Pred Value             0.9920        0.851      0.8064
## Prevalence                 0.1417        0.149      0.1936
## Detection Rate             0.1382        0.000      0.0000
## Detection Prevalence       0.5696        0.000      0.0000
## Balanced Accuracy          0.7366        0.500      0.5000
db_tda_pc_5.40.5_n4_db_lr_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   3.431373e-01   2.467402e-01   3.285637e-01   3.579358e-01   2.605392e-01 
## AccuracyPValue  McnemarPValue 
##   1.075906e-31            NaN
db_tda_pc_5.40.5_n4_db_lr_cf0_ov_acc<-db_tda_pc_5.40.5_n4_db_lr_cf0$overall[1]
db_tda_pc_5.40.5_n4_db_lr_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.5075758   0.9970141      0.9481132      0.9495863 0.9481132
## Class: BOMBAY     1.0000000   1.0000000      1.0000000      1.0000000 1.0000000
## Class: CALI       0.9795501   0.7468672      0.3451009      0.9962853 0.3451009
## Class: DERMASON   0.0000000   1.0000000            NaN      0.7394608        NA
## Class: HOROZ      0.9757785   0.4974300      0.2426850      0.9920273 0.2426850
## Class: SEKER      0.0000000   1.0000000            NaN      0.8509804        NA
## Class: SIRA       0.0000000   1.0000000            NaN      0.8063725        NA
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.5075758 0.6611842 0.09705882     0.04926471
## Class: BOMBAY   1.0000000 1.0000000 0.03823529     0.03823529
## Class: CALI     0.9795501 0.5103889 0.11985294     0.11740196
## Class: DERMASON 0.0000000        NA 0.26053922     0.00000000
## Class: HOROZ    0.9757785 0.3886975 0.14166667     0.13823529
## Class: SEKER    0.0000000        NA 0.14901961     0.00000000
## Class: SIRA     0.0000000        NA 0.19362745     0.00000000
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA           0.05196078         0.7522949
## Class: BOMBAY             0.03823529         1.0000000
## Class: CALI               0.34019608         0.8632086
## Class: DERMASON           0.00000000         0.5000000
## Class: HOROZ              0.56960784         0.7366043
## Class: SEKER              0.00000000         0.5000000
## Class: SIRA               0.00000000         0.5000000
db_tda_pc_5.40.5_n4_db_lr_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n4_db_lr_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted LR vs. tda-assisted LR classifiers

### 3-fold diff

diff_drybean_tda_pca_5.40.5_lr_n4_3_fold<-(db_lr_fit_re - db_tda_pc_5.40.5_n4_lr_fit_re)
diff_drybean_tda_pca_5.40.5_lr_n4_3_fold
##      Accuracy
## 1 -0.05885365
## 2 -0.06579061
## 3 -0.05639629
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_lr.n4_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_lr_n4_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_lr.n4_3_fold
## $probLeft
## [1] 0.75
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_lr.n4_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_lr.n4_3_fold$probLeft/bst_dbf_db_tda_pca_5.40.5_lr.n4_3_fold$probRight
bst_dbf_db_tda_pca_5.40.5_lr.n4_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_lr.n4_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_lr_n4_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_lr.n4_3_fold
## $winLeft
## [1] 0.9913
## 
## $winRope
## [1] 0.0087
## 
## $winRight
## [1] 0
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_lr.n4_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_lr_n4_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_lr.n4_3_fold
## $left
## [1] 0.997932
## 
## $rope
## [1] 0.001005499
## 
## $right
## [1] 0.001062457
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.40.5_lr_n4_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_lr.n4_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_lr_n4_3_fold))
#bf_tda_pca_5.40.5_lr.n4_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.40.5_lr_n4_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_pca_5.40.5_lr_n4_3_fold)
## t = -21.454, df = 2, p-value = 0.002165
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.07244936 -0.04824435
## sample estimates:
##   mean of x 
## -0.06034685
### Test set diff
diff_drybean_tda_pca_5.40.5_lr.n4_test<-(db_lr_cf_ov_acc - db_tda_pc_5.40.5_n4_db_lr_cf0_ov_acc)
diff_drybean_tda_pca_5.40.5_lr.n4_test
##  Accuracy 
## 0.5813725
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_lr.n4_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_lr.n4_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_lr.n4_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_lr.n4_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_lr.n4_test$probLeft/bst_dbf_db_tda_pca_5.40.5_lr.n4_test$probRight
bst_dbf_db_tda_pca_5.40.5_lr.n4_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_lr.n4_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_lr.n4_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_lr.n4_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1598333
## 
## $winRight
## [1] 0.8401667
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_lr.n4_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_lr.n4_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_lr.n4_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_lr.n4_test)))

#BayesFactor
#bf_tda_pca_5.40.5_lr.n4_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_lr.n4_test)) #bf_tda_pca_5.40.5_lr.n4_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_lr.n4_test))

##Node5

#DryBean_TDA_PC_5.40.5_n5_LrFit0 <- train(as.factor(Class) ~ ., 
#                 data = tda.m_dry_bean_dataset_5.40.5.n5.vec, 
#                      family = 'binomial',
#                           method = 'multinom', 
#                      trControl = fitControl,
#                           metric='Accuracy')

#DryBean_TDA_PC_5.40.5_n5_LrFit0
#DryBean_TDA_PC_5.40.5_n5_LrFit0$resample
#db_tda_pc_5.40.5_n5_lr_fit_re<-DryBean_TDA_PC_5.40.5_n5_LrFit0$resample[1]

#summary(DryBean_TDA_PC_5.40.5_n5_LrFit0)

#vip(DryBean_TDA_PC_5.40.5_n5_LrFit0,50) + ggtitle("dryBean_TDA_PCA_5.40.5_n5_Lr1Fit TDA-Assited LR")

# Predict outcome using DryBean_TDA_PC_5.40.5_n5_LrFit0 from training data based on testing data
#pred0 <- predict(DryBean_TDA_PC_5.40.5_n5_LrFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
#db_tda_pc_5.40.5_n5_db_lr_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
#db_tda_pc_5.40.5_n5_db_lr_cf0
#db_tda_pc_5.40.5_n5_db_lr_cf0 
#db_tda_pc_5.40.5_n5_db_lr_cf0$overall
#db_tda_pc_5.40.5_n5_db_lr_cf0_ov_acc<-db_tda_pc_5.40.5_n5_db_lr_cf0$overall[1]
#db_tda_pc_5.40.5_n5_db_lr_cf0$byClass
#db_tda_pc_5.40.5_n5_db_lr_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n5_db_lr_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted LR vs. tda-assisted LR classifiers

### 3-fold diff

#diff_drybean_tda_pca_5.40.5_lr_n5_3_fold<-(db_lr_fit_re - db_tda_pc_5.40.5_n5_lr_fit_re)
#diff_drybean_tda_pca_5.40.5_lr_n5_3_fold

## Bayesian Tests 3-fold diff

# Bayesian Sign Test

#bst_dbf_db_tda_pca_5.40.5_lr.n5_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_lr_n5_3_fold),-0.01,0.01)
#bst_dbf_db_tda_pca_5.40.5_lr.n5_3_fold

# Odds Left Bayesian Sign Test 

#bst_dbf_db_tda_pca_5.40.5_lr.n5_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_lr.n5_3_fold$probLeft/bst_dbf_db_tda_pca_5.40.5_lr.n5_3_fold$probRight
#bst_dbf_db_tda_pca_5.40.5_lr.n5_3_fold_odds.left

# Bayesian Signed Rank Test

#bsr_dbf_db_tda_pca_5.40.5_lr.n5_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_lr_n5_3_fold),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.40.5_lr.n5_3_fold

# Bayesian Correlated Test

#bct_dbf_db_tda_pca_5.40.5_lr.n5_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_lr_n5_3_fold),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.40.5_lr.n5_3_fold

# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_lr_n5_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_lr.n5_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_lr_n5_3_fold))
#bf_tda_pca_5.40.5_lr.n5_3_fold

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_lr_n5_3_fold))


### Test set diff
#diff_drybean_tda_pca_5.40.5_lr.n5_test<-(db_lr_cf_ov_acc - db_tda_pc_5.40.5_n5_db_lr_cf0_ov_acc)
#diff_drybean_tda_pca_5.40.5_lr.n5_test


## Bayesian Tests Test set diff

# Bayesian Sign Test

#bst_dbf_db_tda_pca_5.40.5_lr.n5_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_lr.n5_test),-0.01,0.01)
#bst_dbf_db_tda_pca_5.40.5_lr.n5_test

# Odds Left Bayesian Sign Test 

#bst_dbf_db_tda_pca_5.40.5_lr.n5_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_lr.n5_test$probLeft/bst_dbf_db_tda_pca_5.40.5_lr.n5_test$probRight
#bst_dbf_db_tda_pca_5.40.5_lr.n5_test_odds.left

# Bayesian Signed Rank Test

#bsr_dbf_db_tda_pca_5.40.5_lr.n5_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_lr.n5_test),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.40.5_lr.n5_test

# Bayesian Correlated Test

#bct_dbf_db_tda_pca_5.40.5_lr.n5_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_lr.n5_test),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.40.5_lr.n5_test

# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_lr.n5_test)))

#BayesFactor
#bf_tda_pca_5.40.5_lr.n5_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_lr.n5_test)) #bf_tda_pca_5.40.5_lr.n5_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_lr.n5_test))


##With TDA KDE filter 5 intervals, 50% overlap, 5 bins 
##Node1


DryBean_TDA_KDE_5.40.5_n1_LrFit0 <- train(as.factor(Class) ~ ., 
                 data = tda.m_kde_dry_bean_dataset_5.40.5.n1.vec, 
                      family = 'binomial',
                            method = 'multinom', 
                      trControl = fitControl,
                            metric='Accuracy')
## # weights:  126 (102 variable)
## initial  value 9733.442566 
## iter  10 value 6059.483264
## iter  20 value 4530.201197
## iter  30 value 3972.493806
## iter  40 value 1974.378425
## iter  50 value 835.438825
## iter  60 value 756.577437
## iter  70 value 739.206804
## iter  80 value 725.655896
## iter  90 value 716.340733
## iter 100 value 709.746780
## final  value 709.746780 
## stopped after 100 iterations
## # weights:  126 (102 variable)
## initial  value 9733.442566 
## iter  10 value 6059.483313
## iter  20 value 4530.202241
## iter  30 value 3972.546465
## iter  40 value 1928.535288
## iter  50 value 1025.404161
## iter  60 value 966.795741
## iter  70 value 923.974479
## iter  80 value 895.106905
## iter  90 value 878.168028
## iter 100 value 865.002917
## final  value 865.002917 
## stopped after 100 iterations
## # weights:  126 (102 variable)
## initial  value 9733.442566 
## iter  10 value 6059.483264
## iter  20 value 4530.201205
## iter  30 value 3972.494507
## iter  40 value 1974.450152
## iter  50 value 836.747058
## iter  60 value 758.354311
## iter  70 value 741.934193
## iter  80 value 729.944842
## iter  90 value 722.568423
## iter 100 value 717.853168
## final  value 717.853168 
## stopped after 100 iterations
## # weights:  126 (102 variable)
## initial  value 9733.442566 
## iter  10 value 5879.216581
## iter  20 value 4904.026065
## iter  30 value 4030.988425
## iter  40 value 1390.431316
## iter  50 value 770.859208
## iter  60 value 718.116738
## iter  70 value 693.418416
## iter  80 value 679.833150
## iter  90 value 674.339761
## iter 100 value 667.124353
## final  value 667.124353 
## stopped after 100 iterations
## # weights:  126 (102 variable)
## initial  value 9733.442566 
## iter  10 value 5879.216748
## iter  20 value 4904.032310
## iter  30 value 4031.414250
## iter  40 value 1769.667767
## iter  50 value 1002.017115
## iter  60 value 927.307954
## iter  70 value 862.611326
## iter  80 value 817.705986
## iter  90 value 798.369739
## iter 100 value 791.695521
## final  value 791.695521 
## stopped after 100 iterations
## # weights:  126 (102 variable)
## initial  value 9733.442566 
## iter  10 value 5879.216581
## iter  20 value 4904.026068
## iter  30 value 4030.988619
## iter  40 value 1390.644191
## iter  50 value 771.864692
## iter  60 value 720.421643
## iter  70 value 697.797280
## iter  80 value 686.514702
## iter  90 value 682.052319
## iter 100 value 676.654945
## final  value 676.654945 
## stopped after 100 iterations
## # weights:  126 (102 variable)
## initial  value 9733.442566 
## iter  10 value 6178.246134
## iter  20 value 4862.842258
## iter  30 value 3918.569711
## iter  40 value 1661.855366
## iter  50 value 776.113430
## iter  60 value 712.194885
## iter  70 value 697.007215
## iter  80 value 689.016624
## iter  90 value 682.073465
## iter 100 value 676.214911
## final  value 676.214911 
## stopped after 100 iterations
## # weights:  126 (102 variable)
## initial  value 9733.442566 
## iter  10 value 6178.246215
## iter  20 value 4862.843659
## iter  30 value 3918.650303
## iter  40 value 1748.092048
## iter  50 value 989.174997
## iter  60 value 885.228585
## iter  70 value 827.127153
## iter  80 value 790.753361
## iter  90 value 769.269261
## iter 100 value 764.581721
## final  value 764.581721 
## stopped after 100 iterations
## # weights:  126 (102 variable)
## initial  value 9733.442566 
## iter  10 value 6178.246134
## iter  20 value 4862.842256
## iter  30 value 3918.570134
## iter  40 value 1661.963094
## iter  50 value 775.817583
## iter  60 value 714.043441
## iter  70 value 699.590444
## iter  80 value 692.710767
## iter  90 value 687.284535
## iter 100 value 683.206683
## final  value 683.206683 
## stopped after 100 iterations
## # weights:  126 (102 variable)
## initial  value 14600.163848 
## iter  10 value 10284.583563
## iter  20 value 6872.715309
## iter  30 value 5119.574609
## iter  40 value 2297.884813
## iter  50 value 1195.667653
## iter  60 value 1114.272251
## iter  70 value 1088.761476
## iter  80 value 1073.215807
## iter  90 value 1063.458642
## iter 100 value 1055.748911
## final  value 1055.748911 
## stopped after 100 iterations
DryBean_TDA_KDE_5.40.5_n1_LrFit0
## Penalized Multinomial Regression 
## 
## 7503 samples
##   16 predictor
##    7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 5002, 5002, 5002 
## Resampling results across tuning parameters:
## 
##   decay  Accuracy   Kappa    
##   0e+00  0.9488205  0.9383691
##   1e-04  0.9490870  0.9386901
##   1e-01  0.9462882  0.9353094
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was decay = 1e-04.
DryBean_TDA_KDE_5.40.5_n1_LrFit0$resample
##    Accuracy     Kappa Resample
## 1 0.9484206 0.9379118    Fold2
## 2 0.9516194 0.9417311    Fold1
## 3 0.9472211 0.9364273    Fold3
nb_tda_kde_5.40.5_n1_lr_fit_re<-DryBean_TDA_KDE_5.40.5_n1_LrFit0$resample[1]

summary(DryBean_TDA_KDE_5.40.5_n1_LrFit0)
## Call:
## nnet::multinom(formula = .outcome ~ ., data = dat, decay = param$decay, 
##     family = "binomial")
## 
## Coefficients:
##          (Intercept)        Area   Perimeter MajorAxisLength MinorAxisLength
## BOMBAY    14.1873020 0.001910137 -0.12047989       0.5576488       1.1696856
## CALI      33.5184168 0.003184866 -0.17962449       2.0199688       2.5341731
## DERMASON  38.0431048 0.009141873  0.09035986       1.2498796       1.6271752
## HOROZ      2.7468507 0.007548058  0.08158868       2.1359222       4.1054014
## SEKER     -0.6193353 0.003470135  0.16748254       0.8200154       0.2859032
## SIRA      40.2543945 0.004182689 -0.11497466       2.1790815       2.2248075
##          AspectRation Eccentricity    ConvexArea EquivDiameter     Extent
## BOMBAY      24.667244    16.037489  0.0003234268     -2.221372  4.0030903
## CALI       -76.865527   107.587465 -0.0035012331     -3.779312  4.4126345
## DERMASON   -24.713151    78.391220 -0.0067340966     -4.440149 -9.0606961
## HOROZ        2.685957    95.236092 -0.0067805832     -6.670236 -3.8510845
## SEKER      -51.364667     7.829363 -0.0030895128     -2.078741 -0.1662761
## SIRA       -90.257961   110.918979 -0.0047210431     -4.142088 -7.5258634
##           Solidity roundness Compactness ShapeFactor1 ShapeFactor2 ShapeFactor3
## BOMBAY    14.60846  14.96037    10.46364    0.2626964   0.04382577     7.246138
## CALI      41.21492 -49.64628     1.80558    0.7959565   0.03761688   -35.036270
## DERMASON  36.59657  72.14393    27.16034    0.8139716   0.27362270     7.156249
## HOROZ     29.45577  60.29710   -36.02820    0.9069928  -0.35315550   -74.244914
## SEKER    -21.01748 113.64586    23.86429   -0.8450683   0.04221629    41.893587
## SIRA      31.67910 -15.50600    30.73015   -0.7853421  -0.13548583     2.755482
##          ShapeFactor4
## BOMBAY      14.397608
## CALI        -4.494654
## DERMASON    21.265123
## HOROZ      -11.327108
## SEKER       -1.228420
## SIRA        14.318826
## 
## Std. Errors:
##           (Intercept)         Area    Perimeter MajorAxisLength MinorAxisLength
## BOMBAY   2.554177e-10 2.475484e-05 2.575865e-06    3.503085e-06    2.193147e-06
## CALI     9.443241e-06 2.917189e-04 1.068770e-03    3.082034e-03    3.883226e-03
## DERMASON 1.511044e-05 1.455946e-03 4.704652e-03    1.175883e-03    2.073625e-03
## HOROZ    3.386752e-06 3.762454e-04 1.950812e-03    1.094809e-03    9.967108e-04
## SEKER    4.887807e-06 7.018569e-04 2.217076e-03    4.970320e-04    6.749213e-04
## SIRA     6.609150e-06 4.729673e-04 2.252148e-03    2.444191e-03    2.408816e-03
##          AspectRation Eccentricity   ConvexArea EquivDiameter       Extent
## BOMBAY   2.207759e-08 8.566822e-09 2.493829e-05  8.194967e-08 2.443112e-09
## CALI     2.786441e-05 4.237162e-06 2.891784e-04  1.364790e-03 9.029878e-06
## DERMASON 1.390394e-05 6.118372e-06 1.502147e-03  1.632351e-03 1.246220e-05
## HOROZ    1.211699e-05 3.196096e-06 3.749662e-04  5.266907e-04 2.862897e-06
## SEKER    4.647241e-06 2.592899e-06 7.067817e-04  6.172176e-04 3.857790e-06
## SIRA     2.435897e-05 6.901058e-06 4.780864e-04  8.176692e-04 7.087976e-06
##              Solidity    roundness  Compactness ShapeFactor1 ShapeFactor2
## BOMBAY   5.199186e-10 3.539971e-09 5.741402e-09 3.028653e-11 2.338977e-11
## CALI     9.310259e-06 1.547656e-05 1.685731e-05 1.907626e-08 6.419389e-08
## DERMASON 1.497295e-05 1.532696e-05 1.572940e-05 1.386445e-07 7.296454e-08
## HOROZ    3.326442e-06 3.931668e-06 4.097965e-06 3.840487e-08 1.301324e-08
## SEKER    4.837576e-06 5.025636e-06 4.863660e-06 4.422700e-08 2.507452e-08
## SIRA     6.503149e-06 9.880136e-06 1.186400e-05 4.565126e-08 5.670666e-08
##          ShapeFactor3 ShapeFactor4
## BOMBAY   9.377405e-09 2.278480e-10
## CALI     2.037878e-05 9.279112e-06
## DERMASON 1.592572e-05 1.509422e-05
## HOROZ    4.708549e-06 3.368891e-06
## SEKER    4.802159e-06 4.891802e-06
## SIRA     1.509138e-05 6.618731e-06
## 
## Residual Deviance: 2111.498 
## AIC: 2315.498
vip(DryBean_TDA_KDE_5.40.5_n1_LrFit0,50) + ggtitle("DryBean_TDA_KDE_5.40.5_n1_Lr1Fit TDA-Assited LR")

# Predict outcome using DryBean_TDA_KDE_5.40.5_n1_LrFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.40.5_n1_LrFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.40.5_n1_db_lr_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
nb_tda_kde_5.40.5_n1_db_lr_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      352      0   14        0     1     7    2
##   BOMBAY          0    156    0        0     0     0    0
##   CALI           28      0  459        0     9     0    1
##   DERMASON        0      0    0      825     2     6   18
##   HOROZ           2      0    9        2   556     1    8
##   SEKER           3      0    1       13     0   560   10
##   SIRA           11      0    6      223    10    34  751
## 
## Overall Statistics
##                                          
##                Accuracy : 0.8968         
##                  95% CI : (0.8871, 0.906)
##     No Information Rate : 0.2605         
##     P-Value [Acc > NIR] : < 2.2e-16      
##                                          
##                   Kappa : 0.8757         
##                                          
##  Mcnemar's Test P-Value : NA             
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.88889       1.00000      0.9387          0.7761
## Specificity                  0.99349       1.00000      0.9894          0.9914
## Pos Pred Value               0.93617       1.00000      0.9235          0.9694
## Neg Pred Value               0.98812       1.00000      0.9916          0.9263
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.08627       0.03824      0.1125          0.2022
## Detection Prevalence         0.09216       0.03824      0.1218          0.2086
## Balanced Accuracy            0.94119       1.00000      0.9640          0.8837
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9619       0.9211      0.9506
## Specificity                0.9937       0.9922      0.9137
## Pos Pred Value             0.9619       0.9540      0.7256
## Neg Pred Value             0.9937       0.9863      0.9872
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1363       0.1373      0.1841
## Detection Prevalence       0.1417       0.1439      0.2537
## Balanced Accuracy          0.9778       0.9566      0.9322
nb_tda_kde_5.40.5_n1_db_lr_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      352      0   14        0     1     7    2
##   BOMBAY          0    156    0        0     0     0    0
##   CALI           28      0  459        0     9     0    1
##   DERMASON        0      0    0      825     2     6   18
##   HOROZ           2      0    9        2   556     1    8
##   SEKER           3      0    1       13     0   560   10
##   SIRA           11      0    6      223    10    34  751
## 
## Overall Statistics
##                                          
##                Accuracy : 0.8968         
##                  95% CI : (0.8871, 0.906)
##     No Information Rate : 0.2605         
##     P-Value [Acc > NIR] : < 2.2e-16      
##                                          
##                   Kappa : 0.8757         
##                                          
##  Mcnemar's Test P-Value : NA             
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.88889       1.00000      0.9387          0.7761
## Specificity                  0.99349       1.00000      0.9894          0.9914
## Pos Pred Value               0.93617       1.00000      0.9235          0.9694
## Neg Pred Value               0.98812       1.00000      0.9916          0.9263
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.08627       0.03824      0.1125          0.2022
## Detection Prevalence         0.09216       0.03824      0.1218          0.2086
## Balanced Accuracy            0.94119       1.00000      0.9640          0.8837
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9619       0.9211      0.9506
## Specificity                0.9937       0.9922      0.9137
## Pos Pred Value             0.9619       0.9540      0.7256
## Neg Pred Value             0.9937       0.9863      0.9872
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1363       0.1373      0.1841
## Detection Prevalence       0.1417       0.1439      0.2537
## Balanced Accuracy          0.9778       0.9566      0.9322
nb_tda_kde_5.40.5_n1_db_lr_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.8968137      0.8756826      0.8870710      0.9059839      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
nb_tda_kde_5.40.5_n1_db_lr_cf0_ov_acc<-nb_tda_kde_5.40.5_n1_db_lr_cf0$overall[1]
nb_tda_kde_5.40.5_n1_db_lr_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.8888889   0.9934853      0.9361702      0.9881210 0.9361702
## Class: BOMBAY     1.0000000   1.0000000      1.0000000      1.0000000 1.0000000
## Class: CALI       0.9386503   0.9894180      0.9235412      0.9916271 0.9235412
## Class: DERMASON   0.7761054   0.9913822      0.9694477      0.9262930 0.9694477
## Class: HOROZ      0.9619377   0.9937179      0.9619377      0.9937179 0.9619377
## Class: SEKER      0.9210526   0.9922235      0.9540034      0.9862582 0.9540034
## Class: SIRA       0.9506329   0.9136778      0.7256039      0.9871921 0.7256039
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.8888889 0.9119171 0.09705882     0.08627451
## Class: BOMBAY   1.0000000 1.0000000 0.03823529     0.03823529
## Class: CALI     0.9386503 0.9310345 0.11985294     0.11250000
## Class: DERMASON 0.7761054 0.8620690 0.26053922     0.20220588
## Class: HOROZ    0.9619377 0.9619377 0.14166667     0.13627451
## Class: SEKER    0.9210526 0.9372385 0.14901961     0.13725490
## Class: SIRA     0.9506329 0.8230137 0.19362745     0.18406863
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA           0.09215686         0.9411871
## Class: BOMBAY             0.03823529         1.0000000
## Class: CALI               0.12181373         0.9640341
## Class: DERMASON           0.20857843         0.8837438
## Class: HOROZ              0.14166667         0.9778278
## Class: SEKER              0.14387255         0.9566381
## Class: SIRA               0.25367647         0.9321554
nb_tda_kde_5.40.5_n1_db_lr_cf0_pre_rec_f1<-nb_tda_kde_5.40.5_n1_db_lr_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_kde_5.40.5_lr_n1_3_fold<-(db_lr_fit_re - nb_tda_kde_5.40.5_n1_lr_fit_re)
diff_drybean_tda_kde_5.40.5_lr_n1_3_fold
##      Accuracy
## 1 -0.01737529
## 2 -0.02747708
## 3 -0.02368430
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_lr.n1_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_lr_n1_3_fold),-0.01,0.01)
bst_tda_kde_5.40.5_lr.n1_3_fold
## $probLeft
## [1] 0.75
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_lr.n1_3_fold_odds.left<-bst_tda_kde_5.40.5_lr.n1_3_fold$probLeft/bst_tda_kde_5.40.5_lr.n1_3_fold$probRight
bst_tda_kde_5.40.5_lr.n1_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_lr.n1_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_lr_n1_3_fold),-0.01,0.01)
bsr_tda_kde_5.40.5_lr.n1_3_fold
## $winLeft
## [1] 0.9620333
## 
## $winRope
## [1] 0.03796667
## 
## $winRight
## [1] 0
# Bayesian Correlated Test

bct_tda_kde_5.40.5_lr.n1_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_lr_n1_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_lr.n1_3_fold
## $left
## [1] 0.9682372
## 
## $rope
## [1] 0.02648391
## 
## $right
## [1] 0.005278865
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.40.5_lr_n1_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_kde_5.40.5_lr.n1_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_lr_n1_3_fold))
#bf_tda_kde_5.40.5_lr.n1_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.40.5_lr_n1_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_kde_5.40.5_lr_n1_3_fold)
## t = -7.7544, df = 2, p-value = 0.01623
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.03552175 -0.01016936
## sample estimates:
##   mean of x 
## -0.02284556
### Test set diff
diff_drybean_tda_kde_5.40.5_lr.n1_test<-(db_svm_cf_ov_acc - nb_tda_kde_5.40.5_n1_db_lr_cf0_ov_acc)
diff_drybean_tda_kde_5.40.5_lr.n1_test
##   Accuracy 
## 0.03063725
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_lr.n1_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_lr.n1_test),-0.01,0.01)
bst_tda_kde_5.40.5_lr.n1_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_lr.n1_test_odds.left<-bst_tda_kde_5.40.5_lr.n1_test$probLeft/bst_tda_kde_5.40.5_lr.n1_test$probRight
bst_tda_kde_5.40.5_lr.n1_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_lr.n1_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_lr.n1_test),-0.01,0.01)
bsr_tda_kde_5.40.5_lr.n1_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1599
## 
## $winRight
## [1] 0.8401
# Bayesian Correlated Test

bct_tda_kde_5.40.5_lr.n1_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_lr.n1_test),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_lr.n1_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_lr.n1_test)))

#BayesFactor
#bf_tda_kde_5.40.5_lr.n1_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_lr.n1_test)) #bf_tda_pca_5.40.5_lr.n1_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_lr.n1_test))


##With TDA KDE filter 5 intervals, 50% overlap, 5 bins 
##Node2

DryBean_TDA_KDE_5.40.5_n2_LrFit0 <- train(as.factor(Class) ~ ., 
                 data = tda.m_kde_dry_bean_dataset_5.40.5.n2.vec, 
                      family = 'binomial',
                            method = 'multinom', 
                      trControl = fitControl,
                            metric='Accuracy')
## # weights:  108 (85 variable)
## initial  value 8363.933202 
## iter  10 value 4529.405865
## iter  20 value 2975.194461
## iter  30 value 1760.433512
## iter  40 value 793.794227
## iter  50 value 763.471977
## iter  60 value 745.234855
## iter  70 value 739.382643
## iter  80 value 732.993540
## iter  90 value 728.217000
## iter 100 value 722.392251
## final  value 722.392251 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 8363.933202 
## iter  10 value 4529.406595
## iter  20 value 2975.201500
## iter  30 value 1781.819801
## iter  40 value 910.404722
## iter  50 value 835.909962
## iter  60 value 816.366257
## iter  70 value 809.202983
## iter  80 value 807.716542
## iter  90 value 807.097612
## iter 100 value 806.883085
## final  value 806.883085 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 8363.933202 
## iter  10 value 4529.405865
## iter  20 value 2975.194369
## iter  30 value 1760.392279
## iter  40 value 794.275196
## iter  50 value 764.476650
## iter  60 value 747.322616
## iter  70 value 742.090976
## iter  80 value 736.593332
## iter  90 value 732.779834
## iter 100 value 728.570481
## final  value 728.570481 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 8365.724962 
## iter  10 value 4668.934489
## iter  20 value 3011.908700
## iter  30 value 2055.850719
## iter  40 value 753.397906
## iter  50 value 713.344949
## iter  60 value 696.277845
## iter  70 value 685.083487
## iter  80 value 677.606203
## iter  90 value 673.303612
## iter 100 value 670.969909
## final  value 670.969909 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 8365.724962 
## iter  10 value 4668.935267
## iter  20 value 3011.916016
## iter  30 value 2063.154118
## iter  40 value 869.744551
## iter  50 value 771.494679
## iter  60 value 756.950826
## iter  70 value 749.350767
## iter  80 value 746.248577
## iter  90 value 745.097985
## iter 100 value 744.987860
## final  value 744.987860 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 8365.724962 
## iter  10 value 4668.934491
## iter  20 value 3011.908728
## iter  30 value 2055.869811
## iter  40 value 753.869284
## iter  50 value 714.486400
## iter  60 value 698.550409
## iter  70 value 688.821008
## iter  80 value 682.561772
## iter  90 value 679.283684
## iter 100 value 677.572120
## final  value 677.572120 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 8362.141443 
## iter  10 value 5188.503036
## iter  20 value 3702.806714
## iter  30 value 2340.505925
## iter  40 value 850.301759
## iter  50 value 783.691915
## iter  60 value 764.838663
## iter  70 value 753.717350
## iter  80 value 745.419695
## iter  90 value 739.198003
## iter 100 value 735.698477
## final  value 735.698477 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 8362.141443 
## iter  10 value 5188.503244
## iter  20 value 3702.817208
## iter  30 value 2577.547595
## iter  40 value 993.970023
## iter  50 value 861.361209
## iter  60 value 842.043854
## iter  70 value 831.511415
## iter  80 value 828.470743
## iter  90 value 827.677085
## iter 100 value 827.413071
## final  value 827.413071 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 8362.141443 
## iter  10 value 5188.503036
## iter  20 value 3702.806718
## iter  30 value 2340.762914
## iter  40 value 850.715824
## iter  50 value 784.704543
## iter  60 value 766.799887
## iter  70 value 756.677450
## iter  80 value 749.533503
## iter  90 value 744.672385
## iter 100 value 742.208347
## final  value 742.208347 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 12545.899804 
## iter  10 value 7697.972917
## iter  20 value 5416.041157
## iter  30 value 3014.404294
## iter  40 value 1186.881966
## iter  50 value 1136.218786
## iter  60 value 1109.476463
## iter  70 value 1101.067754
## iter  80 value 1094.292476
## iter  90 value 1084.657983
## iter 100 value 1081.524740
## final  value 1081.524740 
## stopped after 100 iterations
DryBean_TDA_KDE_5.40.5_n2_LrFit0
## Penalized Multinomial Regression 
## 
## 7002 samples
##   16 predictor
##    6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 4668, 4669, 4667 
## Resampling results across tuning parameters:
## 
##   decay  Accuracy   Kappa    
##   0e+00  0.9487283  0.9340500
##   1e-04  0.9492996  0.9347812
##   1e-01  0.9453007  0.9296161
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was decay = 1e-04.
DryBean_TDA_KDE_5.40.5_n2_LrFit0$resample
##    Accuracy     Kappa Resample
## 1 0.9459923 0.9305013    Fold2
## 2 0.9520137 0.9382914    Fold1
## 3 0.9498929 0.9355508    Fold3
nb_tda_kde_5.40.5_n2_lr_fit_re<-DryBean_TDA_KDE_5.40.5_n2_LrFit0$resample[1]

summary(DryBean_TDA_KDE_5.40.5_n2_LrFit0)
## Call:
## nnet::multinom(formula = .outcome ~ ., data = dat, decay = param$decay, 
##     family = "binomial")
## 
## Coefficients:
##          (Intercept)        Area    Perimeter MajorAxisLength MinorAxisLength
## CALI       14.258370 0.002786820 -0.165385866       2.3087941       2.7271582
## DERMASON   23.665719 0.001510649 -0.014862770       1.5566624       1.0250175
## HOROZ     -11.326835 0.008371033 -0.008443637       2.1046390       4.6884226
## SEKER      -9.714985 0.003289476  0.072735728      -0.1173257      -0.3014453
## SIRA       60.936084 0.003769916 -0.400787685       2.2712774       2.7064724
##          AspectRation Eccentricity   ConvexArea EquivDiameter     Extent
## CALI        -45.94758     12.12644 -0.003111714    -4.3420987   3.530103
## DERMASON    -66.71197     36.70367 -0.003553877    -2.4079705 -18.290885
## HOROZ        62.32881     52.86067 -0.006832751    -7.0692336  -6.712081
## SEKER        27.89356    -65.63539 -0.002755505    -0.1773836 -13.543652
## SIRA        -59.35067    102.97293 -0.004680084    -3.4967537 -10.810594
##           Solidity  roundness Compactness ShapeFactor1 ShapeFactor2
## CALI      18.51498  -28.36063   13.441361   0.24706329   0.15448412
## DERMASON  12.47553   48.53314   21.553689   0.04367718   0.07710992
## HOROZ     27.41676   24.89534  -35.768760   0.14648568  -0.36569180
## SEKER    -23.42929   78.75839    8.971654  -0.18812953   0.11987296
## SIRA      34.19692 -143.16977   42.156966   0.98301266   0.32362909
##          ShapeFactor3 ShapeFactor4
## CALI         9.883379     5.943753
## DERMASON     7.673164     8.978022
## HOROZ      -52.446071    -6.645987
## SEKER       34.326637     7.342270
## SIRA         7.993418    21.954292
## 
## Std. Errors:
##           (Intercept)         Area   Perimeter MajorAxisLength MinorAxisLength
## CALI     4.613621e-06 0.0005644030 0.001926715    0.0009832870    0.0008651138
## DERMASON 9.920513e-06 0.0010607738 0.003393116    0.0018978598    0.0020829211
## HOROZ    4.653529e-06 0.0006802816 0.002082293    0.0008633007    0.0007582899
## SEKER    8.533448e-06 0.0012122352 0.003264158    0.0006686185    0.0014988936
## SIRA     8.794368e-06 0.0007047805 0.002665055    0.0036888613    0.0037452725
##          AspectRation Eccentricity   ConvexArea EquivDiameter       Extent
## CALI     8.952414e-06 3.375103e-06 0.0005620807  0.0006341793 3.711622e-06
## DERMASON 2.152876e-05 8.089544e-06 0.0010782060  0.0010730763 9.371575e-06
## HOROZ    7.442639e-06 3.240741e-06 0.0006770147  0.0006821488 4.492655e-06
## SEKER    6.835039e-06 3.129554e-06 0.0012179411  0.0010586643 7.487998e-06
## SIRA     3.922985e-05 1.206936e-05 0.0007096906  0.0010760031 1.042811e-05
##              Solidity    roundness  Compactness ShapeFactor1 ShapeFactor2
## CALI     4.553898e-06 4.996499e-06 4.854145e-06 3.684644e-08 1.795396e-08
## DERMASON 9.797809e-06 1.039456e-05 1.308737e-05 8.973257e-08 6.855666e-08
## HOROZ    4.571632e-06 4.636117e-06 4.466641e-06 3.681869e-08 1.511064e-08
## SEKER    8.446777e-06 8.263454e-06 9.465061e-06 6.438619e-08 3.903970e-08
## SIRA     8.665648e-06 1.461838e-05 1.893891e-05 5.306657e-08 1.003184e-07
##          ShapeFactor3 ShapeFactor4
## CALI     5.112119e-06 4.578253e-06
## DERMASON 1.552199e-05 9.916891e-06
## HOROZ    4.289408e-06 4.590739e-06
## SEKER    9.914373e-06 8.536828e-06
## SIRA     2.502352e-05 8.812527e-06
## 
## Residual Deviance: 2163.049 
## AIC: 2333.049
vip(DryBean_TDA_KDE_5.40.5_n2_LrFit0,50) + ggtitle("DryBean_TDA_KDE_5.40.5_n2_Lr1Fit TDA-Assited LR")

# Predict outcome using DryBean_TDA_KDE_5.40.5_n2_LrFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.40.5_n2_LrFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.40.5_n2_db_lr_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length

## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
nb_tda_kde_5.40.5_n2_db_lr_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      344      0   13        1     1     7    5
##   BOMBAY          0      0    0        0     0     0    0
##   CALI           26      0  462        0    19     0    1
##   DERMASON        0      0    0      960     3     7   63
##   HOROZ           8    156    6        9   545     1    8
##   SEKER           3      0    1       14     0   569    8
##   SIRA           15      0    7       79    10    24  705
## 
## Overall Statistics
##                                           
##                Accuracy : 0.8787          
##                  95% CI : (0.8683, 0.8885)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.8526          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.86869       0.00000      0.9448          0.9031
## Specificity                  0.99267       1.00000      0.9872          0.9758
## Pos Pred Value               0.92722           NaN      0.9094          0.9293
## Neg Pred Value               0.98598       0.96176      0.9924          0.9662
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.08431       0.00000      0.1132          0.2353
## Detection Prevalence         0.09093       0.00000      0.1245          0.2532
## Balanced Accuracy            0.93068       0.50000      0.9660          0.9395
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9429       0.9359      0.8924
## Specificity                0.9463       0.9925      0.9590
## Pos Pred Value             0.7435       0.9563      0.8393
## Neg Pred Value             0.9901       0.9888      0.9738
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1336       0.1395      0.1728
## Detection Prevalence       0.1797       0.1458      0.2059
## Balanced Accuracy          0.9446       0.9642      0.9257
nb_tda_kde_5.40.5_n2_db_lr_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      344      0   13        1     1     7    5
##   BOMBAY          0      0    0        0     0     0    0
##   CALI           26      0  462        0    19     0    1
##   DERMASON        0      0    0      960     3     7   63
##   HOROZ           8    156    6        9   545     1    8
##   SEKER           3      0    1       14     0   569    8
##   SIRA           15      0    7       79    10    24  705
## 
## Overall Statistics
##                                           
##                Accuracy : 0.8787          
##                  95% CI : (0.8683, 0.8885)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.8526          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.86869       0.00000      0.9448          0.9031
## Specificity                  0.99267       1.00000      0.9872          0.9758
## Pos Pred Value               0.92722           NaN      0.9094          0.9293
## Neg Pred Value               0.98598       0.96176      0.9924          0.9662
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.08431       0.00000      0.1132          0.2353
## Detection Prevalence         0.09093       0.00000      0.1245          0.2532
## Balanced Accuracy            0.93068       0.50000      0.9660          0.9395
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9429       0.9359      0.8924
## Specificity                0.9463       0.9925      0.9590
## Pos Pred Value             0.7435       0.9563      0.8393
## Neg Pred Value             0.9901       0.9888      0.9738
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1336       0.1395      0.1728
## Detection Prevalence       0.1797       0.1458      0.2059
## Balanced Accuracy          0.9446       0.9642      0.9257
nb_tda_kde_5.40.5_n2_db_lr_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.8786765      0.8526265      0.8682634      0.8885438      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
nb_tda_kde_5.40.5_n2_db_lr_cf0_ov_acc<-nb_tda_kde_5.40.5_n2_db_lr_cf0$overall[1]
nb_tda_kde_5.40.5_n2_db_lr_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.8686869   0.9926710      0.9272237      0.9859800 0.9272237
## Class: BOMBAY     0.0000000   1.0000000            NaN      0.9617647        NA
## Class: CALI       0.9447853   0.9871902      0.9094488      0.9924412 0.9094488
## Class: DERMASON   0.9031044   0.9758038      0.9293320      0.9661963 0.9293320
## Class: HOROZ      0.9429066   0.9463164      0.7435198      0.9901404 0.7435198
## Class: SEKER      0.9358553   0.9925115      0.9563025      0.9888092 0.9563025
## Class: SIRA       0.8924051   0.9589666      0.8392857      0.9737654 0.8392857
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.8686869 0.8970013 0.09705882     0.08431373
## Class: BOMBAY   0.0000000        NA 0.03823529     0.00000000
## Class: CALI     0.9447853 0.9267803 0.11985294     0.11323529
## Class: DERMASON 0.9031044 0.9160305 0.26053922     0.23529412
## Class: HOROZ    0.9429066 0.8314264 0.14166667     0.13357843
## Class: SEKER    0.9358553 0.9459684 0.14901961     0.13946078
## Class: SIRA     0.8924051 0.8650307 0.19362745     0.17279412
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA           0.09093137         0.9306789
## Class: BOMBAY             0.00000000         0.5000000
## Class: CALI               0.12450980         0.9659877
## Class: DERMASON           0.25318627         0.9394541
## Class: HOROZ              0.17965686         0.9446115
## Class: SEKER              0.14583333         0.9641834
## Class: SIRA               0.20588235         0.9256858
nb_tda_kde_5.40.5_n2_db_lr_cf0_pre_rec_f1<-nb_tda_kde_5.40.5_n2_db_lr_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_kde_5.40.5_lr_n2_3_fold<-(db_lr_fit_re - nb_tda_kde_5.40.5_n2_lr_fit_re)
diff_drybean_tda_kde_5.40.5_lr_n2_3_fold
##      Accuracy
## 1 -0.01494694
## 2 -0.02787144
## 3 -0.02635612
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_lr.n2_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_lr_n2_3_fold),-0.01,0.01)
bst_tda_kde_5.40.5_lr.n2_3_fold
## $probLeft
## [1] 0.75
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_lr.n2_3_fold_odds.left<-bst_tda_kde_5.40.5_lr.n2_3_fold$probLeft/bst_tda_kde_5.40.5_lr.n2_3_fold$probRight
bst_tda_kde_5.40.5_lr.n2_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_lr.n2_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_lr_n2_3_fold),-0.01,0.01)
bsr_tda_kde_5.40.5_lr.n2_3_fold
## $winLeft
## [1] 0.9648667
## 
## $winRope
## [1] 0.03513333
## 
## $winRight
## [1] 0
# Bayesian Correlated Test

bct_tda_kde_5.40.5_lr.n2_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_lr_n2_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_lr.n2_3_fold
## $left
## [1] 0.9453966
## 
## $rope
## [1] 0.04475193
## 
## $right
## [1] 0.009851494
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.40.5_lr_n2_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_kde_5.40.5_lr.n2_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_lr_n2_3_fold))
#bf_tda_kde_5.40.5_lr.n2_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.40.5_lr_n2_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_kde_5.40.5_lr_n2_3_fold)
## t = -5.6527, df = 2, p-value = 0.0299
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.040609262 -0.005507071
## sample estimates:
##   mean of x 
## -0.02305817
### Test set diff
diff_drybean_tda_kde_5.40.5_lr.n2_test<-(db_svm_cf_ov_acc - nb_tda_kde_5.40.5_n2_db_lr_cf0_ov_acc)
diff_drybean_tda_kde_5.40.5_lr.n2_test
##   Accuracy 
## 0.04877451
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_lr.n2_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_lr.n2_test),-0.01,0.01)
bst_tda_kde_5.40.5_lr.n2_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_lr.n2_test_odds.left<-bst_tda_kde_5.40.5_lr.n2_test$probLeft/bst_tda_kde_5.40.5_lr.n2_test$probRight
bst_tda_kde_5.40.5_lr.n2_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_lr.n2_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_lr.n2_test),-0.01,0.01)
bsr_tda_kde_5.40.5_lr.n2_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1596333
## 
## $winRight
## [1] 0.8403667
# Bayesian Correlated Test

bct_tda_kde_5.40.5_lr.n2_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_lr.n2_test),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_lr.n2_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_lr.n2_test)))

#BayesFactor
#bf_tda_kde_5.40.5_lr.n2_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_lr.n2_test)) #bf_tda_pca_5.40.5_lr.n2_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_lr.n2_test))

##Node3

DryBean_TDA_KDE_5.40.5_n3_LrFit0 <- train(as.factor(Class) ~ ., 
                 data = tda.m_kde_dry_bean_dataset_5.40.5.n3.vec, 
                      family = 'binomial',
                            method = 'multinom', 
                      trControl = fitControl,
                            metric='Accuracy')
## # weights:  108 (85 variable)
## initial  value 4194.508917 
## iter  10 value 1501.430592
## iter  20 value 1188.490036
## iter  30 value 741.090249
## iter  40 value 585.123317
## iter  50 value 563.112608
## iter  60 value 555.381005
## iter  70 value 547.163422
## iter  80 value 544.993611
## iter  90 value 543.285316
## iter 100 value 542.010568
## final  value 542.010568 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 4194.508917 
## iter  10 value 1501.431143
## iter  20 value 1188.495968
## iter  30 value 762.055029
## iter  40 value 606.685797
## iter  50 value 591.334111
## iter  60 value 590.277173
## iter  70 value 589.511194
## iter  80 value 589.457278
## iter  90 value 589.435230
## iter 100 value 589.433980
## final  value 589.433980 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 4194.508917 
## iter  10 value 1501.430592
## iter  20 value 1188.490032
## iter  30 value 741.147495
## iter  40 value 585.282752
## iter  50 value 563.816355
## iter  60 value 557.092187
## iter  70 value 550.595444
## iter  80 value 549.045571
## iter  90 value 547.859955
## iter 100 value 547.208330
## final  value 547.208330 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 4196.300677 
## iter  10 value 1466.957586
## iter  20 value 1165.680062
## iter  30 value 713.989079
## iter  40 value 574.017824
## iter  50 value 554.486127
## iter  60 value 547.044841
## iter  70 value 537.159120
## iter  80 value 535.330260
## iter  90 value 532.156078
## iter 100 value 531.065619
## final  value 531.065619 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 4196.300677 
## iter  10 value 1466.958126
## iter  20 value 1165.686842
## iter  30 value 724.548001
## iter  40 value 588.704164
## iter  50 value 578.871648
## iter  60 value 577.962916
## iter  70 value 577.308386
## iter  80 value 577.243138
## iter  90 value 577.204032
## iter 100 value 577.198629
## final  value 577.198629 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 4196.300677 
## iter  10 value 1466.957586
## iter  20 value 1165.680103
## iter  30 value 714.012458
## iter  40 value 574.176158
## iter  50 value 555.336695
## iter  60 value 548.858975
## iter  70 value 541.006354
## iter  80 value 539.689043
## iter  90 value 538.036618
## iter 100 value 537.357309
## final  value 537.357309 
## stopped after 100 iterations
## Warning in nnet::multinom(.outcome ~ ., data = dat, decay = param$decay, :
## group 'CALI' is empty
## # weights:  90 (68 variable)
## initial  value 3764.475277 
## iter  10 value 1076.736535
## iter  20 value 978.645184
## iter  30 value 628.178382
## iter  40 value 599.952990
## iter  50 value 586.626278
## iter  60 value 577.400302
## iter  70 value 574.268150
## iter  80 value 568.324523
## iter  90 value 566.262803
## iter 100 value 564.852887
## final  value 564.852887 
## stopped after 100 iterations
## Warning in nnet::multinom(.outcome ~ ., data = dat, decay = param$decay, :
## group 'CALI' is empty
## # weights:  90 (68 variable)
## initial  value 3764.475277 
## iter  10 value 1076.738104
## iter  20 value 978.710253
## iter  30 value 641.794946
## iter  40 value 623.822100
## iter  50 value 621.752188
## iter  60 value 621.367087
## iter  70 value 621.362652
## final  value 621.362479 
## converged
## Warning in nnet::multinom(.outcome ~ ., data = dat, decay = param$decay, :
## group 'CALI' is empty
## # weights:  90 (68 variable)
## initial  value 3764.475277 
## iter  10 value 1076.736536
## iter  20 value 978.644837
## iter  30 value 628.222304
## iter  40 value 600.392431
## iter  50 value 588.159277
## iter  60 value 580.620072
## iter  70 value 578.270958
## iter  80 value 574.523290
## iter  90 value 573.490643
## iter 100 value 572.638542
## final  value 572.638542 
## stopped after 100 iterations
## # weights:  108 (85 variable)
## initial  value 6290.867496 
## iter  10 value 1864.536088
## iter  20 value 1214.473661
## iter  30 value 1007.066006
## iter  40 value 886.163908
## iter  50 value 863.447491
## iter  60 value 847.573104
## iter  70 value 844.321998
## iter  80 value 840.833495
## iter  90 value 839.184336
## iter 100 value 838.304856
## final  value 838.304856 
## stopped after 100 iterations
DryBean_TDA_KDE_5.40.5_n3_LrFit0
## Penalized Multinomial Regression 
## 
## 3511 samples
##   16 predictor
##    6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 2341, 2342, 2339 
## Resampling results across tuning parameters:
## 
##   decay  Accuracy   Kappa    
##   0e+00  0.9119798  0.8667027
##   1e-04  0.9119801  0.8667075
##   1e-01  0.9077119  0.8602176
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was decay = 1e-04.
DryBean_TDA_KDE_5.40.5_n3_LrFit0$resample
##    Accuracy     Kappa Resample
## 1 0.8999145 0.8482197    Fold2
## 2 0.9111111 0.8654072    Fold1
## 3 0.9249147 0.8864955    Fold3
nb_tda_kde_5.40.5_n3_lr_fit_re<-DryBean_TDA_KDE_5.40.5_n2_LrFit0$resample[1]

summary(DryBean_TDA_KDE_5.40.5_n3_LrFit0)
## Call:
## nnet::multinom(formula = .outcome ~ ., data = dat, decay = param$decay, 
##     family = "binomial")
## 
## Coefficients:
##          (Intercept)         Area   Perimeter MajorAxisLength MinorAxisLength
## CALI        2.005974 -0.022194604 -0.40534324     -12.4609350      -19.195421
## DERMASON  -21.736382  0.001486469 -0.08635313       1.3903655        2.155261
## HOROZ       6.116376  0.009766171 -0.05505338       2.7822719        4.468584
## SEKER     -12.684383  0.013452529 -0.04898949      -0.3480099       -1.980234
## SIRA       29.219868  0.005288503 -0.18154725       2.3608652        3.619749
##          AspectRation Eccentricity   ConvexArea EquivDiameter    Extent
## CALI         4.278773    -1.818747  0.021840514     32.313579 -19.67185
## DERMASON     1.090549     9.835796 -0.007524805     -1.588432 -27.97048
## HOROZ       28.078349    22.029341 -0.010829198     -6.900758 -31.56502
## SEKER        3.948898  -154.771251 -0.016594774      3.111772 -29.18092
## SIRA       -34.606400   111.142340 -0.006459143     -5.121273 -20.24508
##            Solidity  roundness Compactness ShapeFactor1 ShapeFactor2
## CALI       1.970375   1.971100    2.421876   0.03574853   0.02164315
## DERMASON -33.710481  22.916955  -24.306509  -0.30904693  -0.23321027
## HOROZ      8.131654  10.996324   -2.632900   0.24734110  -0.09328766
## SEKER    -15.497369   3.090331   29.723739  -0.54368815   0.49175547
## SIRA      39.215199 -45.439368    3.303731   0.57066829  -0.09243678
##          ShapeFactor3 ShapeFactor4
## CALI         3.097229     2.951293
## DERMASON   -26.776334   -21.128392
## HOROZ       -9.424449     4.909219
## SEKER       78.360792    15.216318
## SIRA       -31.117770     4.577506
## 
## Std. Errors:
##           (Intercept)         Area    Perimeter MajorAxisLength MinorAxisLength
## CALI     3.037860e-08 0.0001426701 1.841498e-05    4.423057e-06    6.778082e-06
## DERMASON 9.056845e-06 0.0022504591 1.993228e-03    3.435719e-03    3.496784e-03
## HOROZ    9.990315e-06 0.0025429288 3.883587e-03    1.436251e-03    8.879165e-04
## SEKER    9.249525e-06 0.0026063592 3.091714e-03    7.712704e-04    1.199584e-03
## SIRA     9.657324e-06 0.0021767226 2.062003e-03    3.426201e-03    3.689496e-03
##          AspectRation Eccentricity   ConvexArea EquivDiameter       Extent
## CALI     4.895298e-08 2.048300e-08 0.0001467784  3.780562e-06 2.269555e-08
## DERMASON 3.748287e-05 1.368599e-05 0.0022133288  9.637397e-04 9.684303e-06
## HOROZ    1.629773e-05 7.970140e-06 0.0024957374  1.129207e-03 7.449418e-06
## SEKER    8.223863e-06 4.271533e-06 0.0025756272  1.036926e-03 7.890147e-06
## SIRA     3.744933e-05 1.313394e-05 0.0021415093  1.059634e-03 1.071252e-05
##              Solidity    roundness  Compactness ShapeFactor1 ShapeFactor2
## CALI     3.282868e-08 6.669255e-08 3.917433e-08 2.320638e-10 1.807046e-10
## DERMASON 8.924768e-06 1.320300e-05 1.927380e-05 6.412941e-08 1.133702e-07
## HOROZ    9.821887e-06 8.480213e-06 7.767455e-06 1.088252e-07 2.448294e-08
## SEKER    9.172683e-06 9.522508e-06 9.219170e-06 8.243931e-08 3.965009e-08
## SIRA     9.538463e-06 1.403405e-05 2.030872e-05 5.599281e-08 1.166531e-07
##          ShapeFactor3 ShapeFactor4
## CALI     4.548933e-08 3.071443e-08
## DERMASON 2.590742e-05 9.073007e-06
## HOROZ    6.027663e-06 9.922019e-06
## SEKER    8.962286e-06 9.244213e-06
## SIRA     2.698879e-05 9.698527e-06
## 
## Residual Deviance: 1676.61 
## AIC: 1846.61
vip(DryBean_TDA_KDE_5.40.5_n3_LrFit0,50) + ggtitle("DryBean_TDA_KDE_5.40.5_n3_Lr1Fit TDA-Assited LR")

# Predict outcome using DryBean_TDA_KDE_5.40.5_n3_LrFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.40.5_n3_LrFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.40.5_n3_db_lr_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length

## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
nb_tda_kde_5.40.5_n3_db_lr_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      389    156  477        1    33    26   28
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     2    1
##   DERMASON        0      0    0      974     3    10   80
##   HOROZ           0      0    3       16   500     0    7
##   SEKER           0      0    0       18     0   554    8
##   SIRA            7      0    9       54    42    16  666
## 
## Overall Statistics
##                                           
##                Accuracy : 0.7556          
##                  95% CI : (0.7421, 0.7688)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.705           
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.98232       0.00000   0.0000000          0.9163
## Specificity                  0.80429       1.00000   0.9991646          0.9692
## Pos Pred Value               0.35045           NaN   0.0000000          0.9128
## Neg Pred Value               0.99764       0.96176   0.8800589          0.9705
## Prevalence                   0.09706       0.03824   0.1198529          0.2605
## Detection Rate               0.09534       0.00000   0.0000000          0.2387
## Detection Prevalence         0.27206       0.00000   0.0007353          0.2615
## Balanced Accuracy            0.89331       0.50000   0.4995823          0.9427
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.8651       0.9112      0.8430
## Specificity                0.9926       0.9925      0.9611
## Pos Pred Value             0.9506       0.9552      0.8388
## Neg Pred Value             0.9781       0.9846      0.9623
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1225       0.1358      0.1632
## Detection Prevalence       0.1289       0.1422      0.1946
## Balanced Accuracy          0.9288       0.9518      0.9021
nb_tda_kde_5.40.5_n3_db_lr_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      389    156  477        1    33    26   28
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     2    1
##   DERMASON        0      0    0      974     3    10   80
##   HOROZ           0      0    3       16   500     0    7
##   SEKER           0      0    0       18     0   554    8
##   SIRA            7      0    9       54    42    16  666
## 
## Overall Statistics
##                                           
##                Accuracy : 0.7556          
##                  95% CI : (0.7421, 0.7688)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.705           
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.98232       0.00000   0.0000000          0.9163
## Specificity                  0.80429       1.00000   0.9991646          0.9692
## Pos Pred Value               0.35045           NaN   0.0000000          0.9128
## Neg Pred Value               0.99764       0.96176   0.8800589          0.9705
## Prevalence                   0.09706       0.03824   0.1198529          0.2605
## Detection Rate               0.09534       0.00000   0.0000000          0.2387
## Detection Prevalence         0.27206       0.00000   0.0007353          0.2615
## Balanced Accuracy            0.89331       0.50000   0.4995823          0.9427
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.8651       0.9112      0.8430
## Specificity                0.9926       0.9925      0.9611
## Pos Pred Value             0.9506       0.9552      0.8388
## Neg Pred Value             0.9781       0.9846      0.9623
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1225       0.1358      0.1632
## Detection Prevalence       0.1289       0.1422      0.1946
## Balanced Accuracy          0.9288       0.9518      0.9021
nb_tda_kde_5.40.5_n3_db_lr_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.7556373      0.7049616      0.7421491      0.7687584      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
nb_tda_kde_5.40.5_n3_db_lr_cf0_ov_acc<-nb_tda_kde_5.40.5_n3_db_lr_cf0$overall[1]
nb_tda_kde_5.40.5_n3_db_lr_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.9823232   0.8042888      0.3504505      0.9976431 0.3504505
## Class: BOMBAY     0.0000000   1.0000000            NaN      0.9617647        NA
## Class: CALI       0.0000000   0.9991646      0.0000000      0.8800589 0.0000000
## Class: DERMASON   0.9162747   0.9691747      0.9128397      0.9704613 0.9128397
## Class: HOROZ      0.8650519   0.9925757      0.9505703      0.9780529 0.9505703
## Class: SEKER      0.9111842   0.9925115      0.9551724      0.9845714 0.9551724
## Class: SIRA       0.8430380   0.9610942      0.8387909      0.9622642 0.8387909
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.9823232 0.5166003 0.09705882     0.09534314
## Class: BOMBAY   0.0000000        NA 0.03823529     0.00000000
## Class: CALI     0.0000000       NaN 0.11985294     0.00000000
## Class: DERMASON 0.9162747 0.9145540 0.26053922     0.23872549
## Class: HOROZ    0.8650519 0.9057971 0.14166667     0.12254902
## Class: SEKER    0.9111842 0.9326599 0.14901961     0.13578431
## Class: SIRA     0.8430380 0.8409091 0.19362745     0.16323529
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA         0.2720588235         0.8933060
## Class: BOMBAY           0.0000000000         0.5000000
## Class: CALI             0.0007352941         0.4995823
## Class: DERMASON         0.2615196078         0.9427247
## Class: HOROZ            0.1289215686         0.9288138
## Class: SEKER            0.1421568627         0.9518479
## Class: SIRA             0.1946078431         0.9020661
nb_tda_kde_5.40.5_n3_db_lr_cf0_pre_rec_f1<-nb_tda_kde_5.40.5_n3_db_lr_cf0$byClass[5:7]


###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_kde_5.40.5_lr_n3_3_fold<-(db_lr_fit_re - nb_tda_kde_5.40.5_n3_lr_fit_re)
diff_drybean_tda_kde_5.40.5_lr_n3_3_fold
##      Accuracy
## 1 -0.01494694
## 2 -0.02787144
## 3 -0.02635612
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_lr.n3_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_lr_n3_3_fold),-0.01,0.01)
bst_tda_kde_5.40.5_lr.n3_3_fold
## $probLeft
## [1] 0.75
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_lr.n3_3_fold_odds.left<-bst_tda_kde_5.40.5_lr.n3_3_fold$probLeft/bst_tda_kde_5.40.5_lr.n3_3_fold$probRight
bst_tda_kde_5.40.5_lr.n3_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_lr.n3_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_lr_n3_3_fold),-0.01,0.01)
bsr_tda_kde_5.40.5_lr.n3_3_fold
## $winLeft
## [1] 0.9622333
## 
## $winRope
## [1] 0.03776667
## 
## $winRight
## [1] 0
# Bayesian Correlated Test

bct_tda_kde_5.40.5_lr.n3_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_lr_n3_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_lr.n3_3_fold
## $left
## [1] 0.9453966
## 
## $rope
## [1] 0.04475193
## 
## $right
## [1] 0.009851494
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.40.5_lr_n3_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_kde_5.40.5_lr.n3_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_lr_n3_3_fold))
#bf_tda_kde_5.40.5_lr.n3_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.40.5_lr_n3_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_kde_5.40.5_lr_n3_3_fold)
## t = -5.6527, df = 2, p-value = 0.0299
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.040609262 -0.005507071
## sample estimates:
##   mean of x 
## -0.02305817
### Test set diff
diff_drybean_tda_kde_5.40.5_lr.n3_test<-(db_svm_cf_ov_acc - nb_tda_kde_5.40.5_n3_db_lr_cf0_ov_acc)
diff_drybean_tda_kde_5.40.5_lr.n3_test
##  Accuracy 
## 0.1718137
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_lr.n3_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_lr.n3_test),-0.01,0.01)
bst_tda_kde_5.40.5_lr.n3_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_lr.n3_test_odds.left<-bst_tda_kde_5.40.5_lr.n3_test$probLeft/bst_tda_kde_5.40.5_lr.n3_test$probRight
bst_tda_kde_5.40.5_lr.n3_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_lr.n2_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_lr.n2_test),-0.01,0.01)
bsr_tda_kde_5.40.5_lr.n2_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1612
## 
## $winRight
## [1] 0.8388
# Bayesian Correlated Test

bct_tda_kde_5.40.5_lr.n3_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_lr.n3_test),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_lr.n3_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_lr.n3_test)))

#BayesFactor
#bf_tda_kde_5.40.5_lr.n3_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_lr.n3_test)) #bf_tda_pca_5.40.5_lr.n3_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_lr.n3_test))

##Node4

DryBean_TDA_KDE_5.40.5_n4_LrFit0 <- train(as.factor(Class) ~ ., 
                 data = tda.m_kde_dry_bean_dataset_5.40.5.n4.vec, 
                      family = 'binomial',
                            method = 'multinom', 
                      trControl = fitControl,
                            metric='Accuracy')
## # weights:  72 (51 variable)
## initial  value 1626.123286 
## iter  10 value 720.671191
## iter  20 value 532.879933
## iter  30 value 474.949742
## iter  40 value 470.725407
## iter  50 value 468.283500
## iter  60 value 467.625199
## iter  70 value 466.959102
## iter  80 value 461.828096
## iter  90 value 459.929792
## iter 100 value 456.891479
## final  value 456.891479 
## stopped after 100 iterations
## # weights:  72 (51 variable)
## initial  value 1626.123286 
## iter  10 value 720.684431
## iter  20 value 540.578907
## iter  30 value 487.429712
## iter  40 value 486.218798
## final  value 486.087150 
## converged
## # weights:  72 (51 variable)
## initial  value 1626.123286 
## iter  10 value 720.671204
## iter  20 value 532.889920
## iter  30 value 475.009897
## iter  40 value 471.074839
## iter  50 value 468.982566
## iter  60 value 468.494464
## iter  70 value 468.031536
## iter  80 value 467.138320
## iter  90 value 467.033190
## iter 100 value 466.960591
## final  value 466.960591 
## stopped after 100 iterations
## # weights:  72 (51 variable)
## initial  value 1624.736991 
## iter  10 value 781.190509
## iter  20 value 541.075655
## iter  30 value 487.290991
## iter  40 value 483.777412
## iter  50 value 481.033469
## iter  60 value 479.484664
## iter  70 value 478.295501
## iter  80 value 476.037894
## iter  90 value 472.027620
## iter 100 value 470.980801
## final  value 470.980801 
## stopped after 100 iterations
## # weights:  72 (51 variable)
## initial  value 1624.736991 
## iter  10 value 781.201726
## iter  20 value 545.404791
## iter  30 value 498.470858
## iter  40 value 497.660956
## iter  50 value 496.966569
## iter  50 value 496.966567
## iter  50 value 496.966567
## final  value 496.966567 
## converged
## # weights:  72 (51 variable)
## initial  value 1624.736991 
## iter  10 value 781.190520
## iter  20 value 541.079652
## iter  30 value 487.365386
## iter  40 value 484.061087
## iter  50 value 481.610828
## iter  60 value 480.358571
## iter  70 value 479.648317
## iter  80 value 478.691068
## iter  90 value 478.057179
## iter 100 value 477.943398
## final  value 477.943398 
## stopped after 100 iterations
## # weights:  72 (51 variable)
## initial  value 1626.123286 
## iter  10 value 752.983341
## iter  20 value 566.626083
## iter  30 value 515.278704
## iter  40 value 509.707541
## iter  50 value 508.824548
## iter  60 value 507.274177
## iter  70 value 506.179782
## iter  80 value 502.930086
## iter  90 value 500.875425
## iter 100 value 499.585046
## final  value 499.585046 
## stopped after 100 iterations
## # weights:  72 (51 variable)
## initial  value 1626.123286 
## iter  10 value 752.999584
## iter  20 value 568.559530
## iter  30 value 521.196806
## iter  40 value 517.343163
## final  value 517.338341 
## converged
## # weights:  72 (51 variable)
## initial  value 1626.123286 
## iter  10 value 752.983356
## iter  20 value 566.627544
## iter  30 value 515.314670
## iter  40 value 509.874066
## iter  50 value 509.085829
## iter  60 value 508.003916
## iter  70 value 507.415184
## iter  80 value 506.492400
## iter  90 value 506.235796
## iter 100 value 506.151775
## final  value 506.151775 
## stopped after 100 iterations
## # weights:  72 (51 variable)
## initial  value 2438.491781 
## iter  10 value 1080.028813
## iter  20 value 822.329903
## iter  30 value 765.256661
## iter  40 value 753.011257
## final  value 752.821685 
## converged
DryBean_TDA_KDE_5.40.5_n4_LrFit0
## Penalized Multinomial Regression 
## 
## 1759 samples
##   16 predictor
##    4 classes: 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 1173, 1172, 1173 
## Resampling results across tuning parameters:
## 
##   decay  Accuracy   Kappa    
##   0e+00  0.8135368  0.6823603
##   1e-04  0.8129660  0.6814350
##   1e-01  0.8180787  0.6887637
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was decay = 0.1.
DryBean_TDA_KDE_5.40.5_n4_LrFit0$resample
##    Accuracy     Kappa Resample
## 1 0.8088737 0.6741923    Fold1
## 2 0.8276451 0.7026546    Fold3
## 3 0.8177172 0.6894441    Fold2
nb_tda_kde_5.40.5_n4_lr_fit_re<-DryBean_TDA_KDE_5.40.5_n4_LrFit0$resample[1]

summary(DryBean_TDA_KDE_5.40.5_n4_LrFit0)
## Call:
## nnet::multinom(formula = .outcome ~ ., data = dat, decay = param$decay, 
##     family = "binomial")
## 
## Coefficients:
##        (Intercept)          Area  Perimeter MajorAxisLength MinorAxisLength
## HOROZ -0.007812297 -6.553801e-04 0.05050997       0.5803191       0.6367674
## SEKER  0.056022025  1.495216e-02 0.15697171      -2.3339996      -2.3837193
## SIRA  -0.018745715 -8.518868e-05 0.08567921       0.5231472       0.8213527
##       AspectRation Eccentricity   ConvexArea EquivDiameter     Extent
## HOROZ  -0.01184771 -0.001732693  0.001596173     -1.588726 -0.1989763
## SEKER   0.06168213 -0.121316864 -0.013734111      4.069019  0.7787332
## SIRA   -0.64058312  0.642991220  0.001135857     -1.793984  4.4085934
##          Solidity   roundness Compactness  ShapeFactor1  ShapeFactor2
## HOROZ -0.01044365 -0.02671530 -0.01586829  8.063949e-05 -8.597689e-05
## SEKER  0.05040623  0.09480424  0.11543286  1.886741e-04  1.117565e-03
## SIRA  -0.02184607 -0.23800259 -0.16046994 -4.177328e-04 -2.110666e-03
##       ShapeFactor3 ShapeFactor4
## HOROZ  -0.02083936  -0.02900588
## SEKER   0.17352208   0.13642031
## SIRA   -0.37720992  -0.22753275
## 
## Std. Errors:
##        (Intercept)         Area    Perimeter MajorAxisLength MinorAxisLength
## HOROZ 5.691889e-07 0.0036282611 0.0004722981    0.0001435033    1.031957e-05
## SEKER 1.786322e-05 0.0019558601 0.0063338673    0.0020005082    1.918749e-03
## SIRA  6.777809e-06 0.0008724001 0.0026992888    0.0012540549    5.885693e-04
##       AspectRation Eccentricity   ConvexArea EquivDiameter       Extent
## HOROZ 1.604190e-06 6.878442e-07 0.0035739428  5.246673e-05 2.207784e-07
## SEKER 2.112890e-05 1.026568e-05 0.0019926361  1.980605e-03 1.343189e-05
## SIRA  1.385025e-05 6.479689e-06 0.0008887527  7.427780e-04 4.756165e-06
##           Solidity    roundness  Compactness ShapeFactor1 ShapeFactor2
## HOROZ 3.819824e-07 2.470640e-07 2.307064e-07 8.553245e-09 1.675254e-10
## SEKER 1.772247e-05 1.698536e-05 1.631721e-05 1.719567e-07 6.755174e-08
## SIRA  6.717698e-06 5.605193e-06 5.127042e-06 7.957354e-08 1.912752e-08
##       ShapeFactor3 ShapeFactor4
## HOROZ 3.483254e-08 4.655267e-07
## SEKER 1.484638e-05 1.787678e-05
## SIRA  4.383074e-06 6.765515e-06
## 
## Residual Deviance: 1505.643 
## AIC: 1607.643
vip(DryBean_TDA_KDE_5.40.5_n4_LrFit0,50) + ggtitle("DryBean_TDA_KDE_5.40.5_n4_Lr1Fit TDA-Assited LR")

# Predict outcome using DryBean_TDA_KDE_5.40.5_n4_LrFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.40.5_n4_LrFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.40.5_n4_db_lr_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length

## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
nb_tda_kde_5.40.5_n4_db_lr_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     0    0
##   DERMASON        0      0    0      984     3    13   77
##   HOROZ           0      0    3        1   447     0    1
##   SEKER          15      9    1       11     0   569   10
##   SIRA          381    147  485       67   128    26  702
## 
## Overall Statistics
##                                           
##                Accuracy : 0.6623          
##                  95% CI : (0.6475, 0.6768)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.5784          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000      0.0000          0.9257
## Specificity                  1.00000       1.00000      1.0000          0.9692
## Pos Pred Value                   NaN           NaN         NaN          0.9136
## Neg Pred Value               0.90294       0.96176      0.8801          0.9737
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.00000       0.00000      0.0000          0.2412
## Detection Prevalence         0.00000       0.00000      0.0000          0.2640
## Balanced Accuracy            0.50000       0.50000      0.5000          0.9474
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.7734       0.9359      0.8886
## Specificity                0.9986       0.9868      0.6249
## Pos Pred Value             0.9889       0.9252      0.3626
## Neg Pred Value             0.9639       0.9887      0.9590
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1096       0.1395      0.1721
## Detection Prevalence       0.1108       0.1507      0.4745
## Balanced Accuracy          0.8860       0.9613      0.7568
nb_tda_kde_5.40.5_n4_db_lr_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     0    0
##   DERMASON        0      0    0      984     3    13   77
##   HOROZ           0      0    3        1   447     0    1
##   SEKER          15      9    1       11     0   569   10
##   SIRA          381    147  485       67   128    26  702
## 
## Overall Statistics
##                                           
##                Accuracy : 0.6623          
##                  95% CI : (0.6475, 0.6768)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.5784          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000      0.0000          0.9257
## Specificity                  1.00000       1.00000      1.0000          0.9692
## Pos Pred Value                   NaN           NaN         NaN          0.9136
## Neg Pred Value               0.90294       0.96176      0.8801          0.9737
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.00000       0.00000      0.0000          0.2412
## Detection Prevalence         0.00000       0.00000      0.0000          0.2640
## Balanced Accuracy            0.50000       0.50000      0.5000          0.9474
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.7734       0.9359      0.8886
## Specificity                0.9986       0.9868      0.6249
## Pos Pred Value             0.9889       0.9252      0.3626
## Neg Pred Value             0.9639       0.9887      0.9590
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1096       0.1395      0.1721
## Detection Prevalence       0.1108       0.1507      0.4745
## Balanced Accuracy          0.8860       0.9613      0.7568
nb_tda_kde_5.40.5_n4_db_lr_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.6622549      0.5784458      0.6475082      0.6767689      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
nb_tda_kde_5.40.5_n4_db_lr_cf0_ov_acc<-nb_tda_kde_5.40.5_n4_db_lr_cf0$overall[1]
nb_tda_kde_5.40.5_n4_db_lr_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.0000000   1.0000000            NaN      0.9029412        NA
## Class: BOMBAY     0.0000000   1.0000000            NaN      0.9617647        NA
## Class: CALI       0.0000000   1.0000000            NaN      0.8801471        NA
## Class: DERMASON   0.9256820   0.9691747      0.9136490      0.9736930 0.9136490
## Class: HOROZ      0.7733564   0.9985722      0.9889381      0.9638920 0.9889381
## Class: SEKER      0.9358553   0.9867512      0.9252033      0.9887446 0.9252033
## Class: SIRA       0.8886076   0.6249240      0.3626033      0.9589552 0.3626033
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.0000000        NA 0.09705882      0.0000000
## Class: BOMBAY   0.0000000        NA 0.03823529      0.0000000
## Class: CALI     0.0000000        NA 0.11985294      0.0000000
## Class: DERMASON 0.9256820 0.9196262 0.26053922      0.2411765
## Class: HOROZ    0.7733564 0.8679612 0.14166667      0.1095588
## Class: SEKER    0.9358553 0.9304988 0.14901961      0.1394608
## Class: SIRA     0.8886076 0.5150404 0.19362745      0.1720588
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA            0.0000000         0.5000000
## Class: BOMBAY              0.0000000         0.5000000
## Class: CALI                0.0000000         0.5000000
## Class: DERMASON            0.2639706         0.9474284
## Class: HOROZ               0.1107843         0.8859643
## Class: SEKER               0.1507353         0.9613032
## Class: SIRA                0.4745098         0.7567658
nb_tda_kde_5.40.5_n4_db_lr_cf0_pre_rec_f1<-nb_tda_kde_5.40.5_n4_db_lr_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_kde_5.40.5_lr_n4_3_fold<-(db_lr_fit_re - nb_tda_kde_5.40.5_n4_lr_fit_re)
diff_drybean_tda_kde_5.40.5_lr_n4_3_fold
##     Accuracy
## 1 0.12217162
## 2 0.09649722
## 3 0.10581961
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_lr.n4_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_lr_n4_3_fold),-0.01,0.01)
bst_tda_kde_5.40.5_lr.n4_3_fold
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_lr.n4_3_fold_odds.left<-bst_tda_kde_5.40.5_lr.n4_3_fold$probLeft/bst_tda_kde_5.40.5_lr.n4_3_fold$probRight
bst_tda_kde_5.40.5_lr.n4_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_lr.n4_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_lr_n4_3_fold),-0.01,0.01)
bsr_tda_kde_5.40.5_lr.n4_3_fold
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.009833333
## 
## $winRight
## [1] 0.9901667
# Bayesian Correlated Test

bct_tda_kde_5.40.5_lr.n4_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_lr_n4_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_lr.n4_3_fold
## $left
## [1] 0.002666858
## 
## $rope
## [1] 0.001183611
## 
## $right
## [1] 0.9961495
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.40.5_lr_n4_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_kde_5.40.5_lr.n4_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_lr_n4_3_fold))
#bf_tda_kde_5.40.5_lr.n4_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.40.5_lr_n4_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_kde_5.40.5_lr_n4_3_fold)
## t = 14.415, df = 2, p-value = 0.004778
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  0.07587747 0.14044816
## sample estimates:
## mean of x 
## 0.1081628
### Test set diff
diff_drybean_tda_kde_5.40.5_lr.n4_test<-(db_svm_cf_ov_acc - nb_tda_kde_5.40.5_n4_db_lr_cf0_ov_acc)
diff_drybean_tda_kde_5.40.5_lr.n4_test
##  Accuracy 
## 0.2651961
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_lr.n4_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_lr.n4_test),-0.01,0.01)
bst_tda_kde_5.40.5_lr.n4_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_lr.n4_test_odds.left<-bst_tda_kde_5.40.5_lr.n4_test$probLeft/bst_tda_kde_5.40.5_lr.n4_test$probRight
bst_tda_kde_5.40.5_lr.n4_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_lr.n4_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_lr.n4_test),-0.01,0.01)
bsr_tda_kde_5.40.5_lr.n4_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1584333
## 
## $winRight
## [1] 0.8415667
# Bayesian Correlated Test

bct_tda_kde_5.40.5_lr.n4_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_lr.n4_test),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_lr.n4_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_lr.n4_test)))

#BayesFactor
#bf_tda_kde_5.40.5_lr.n4_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_lr.n4_test)) #bf_tda_pca_5.40.5_lr.n4_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_lr.n4_test))

##Node5

DryBean_TDA_KDE_5.40.5_n5_LrFit0 <- train(as.factor(Class) ~ ., 
                 data = tda.m_kde_dry_bean_dataset_5.40.5.n5.vec, 
                      family = 'binomial',
                            method = 'multinom', 
                      trControl = fitControl,
                            metric='Accuracy')
## Warning in nnet::multinom(.outcome ~ ., data = dat, decay = param$decay, :
## group 'HOROZ' is empty
## # weights:  54 (34 variable)
## initial  value 565.785329 
## iter  10 value 352.337396
## iter  20 value 277.590063
## iter  30 value 276.810897
## iter  40 value 274.674321
## iter  50 value 273.307633
## iter  60 value 271.017584
## iter  70 value 270.838915
## iter  80 value 270.802325
## iter  90 value 270.732885
## iter 100 value 270.297104
## final  value 270.297104 
## stopped after 100 iterations
## Warning in nnet::multinom(.outcome ~ ., data = dat, decay = param$decay, :
## group 'HOROZ' is empty
## # weights:  54 (34 variable)
## initial  value 565.785329 
## iter  10 value 352.353750
## iter  20 value 284.972032
## final  value 284.969223 
## converged
## Warning in nnet::multinom(.outcome ~ ., data = dat, decay = param$decay, :
## group 'HOROZ' is empty
## # weights:  54 (34 variable)
## initial  value 565.785329 
## iter  10 value 352.337413
## iter  20 value 277.610569
## iter  30 value 276.873824
## iter  40 value 275.212684
## iter  50 value 274.513727
## iter  60 value 274.315250
## iter  70 value 274.311017
## iter  80 value 274.310530
## iter  90 value 274.310289
## iter 100 value 274.310095
## final  value 274.310095 
## stopped after 100 iterations
## # weights:  72 (51 variable)
## initial  value 716.714185 
## iter  10 value 405.259148
## iter  20 value 291.766949
## iter  30 value 273.802517
## iter  40 value 272.493368
## iter  50 value 271.561546
## iter  60 value 269.278114
## iter  70 value 269.000075
## iter  80 value 268.936560
## iter  90 value 266.698896
## iter 100 value 265.946636
## final  value 265.946636 
## stopped after 100 iterations
## # weights:  72 (51 variable)
## initial  value 716.714185 
## iter  10 value 405.261860
## iter  20 value 294.767039
## iter  30 value 283.649191
## iter  40 value 282.839990
## iter  50 value 282.753839
## iter  50 value 282.753836
## iter  50 value 282.753836
## final  value 282.753836 
## converged
## # weights:  72 (51 variable)
## initial  value 716.714185 
## iter  10 value 405.259151
## iter  20 value 291.775701
## iter  30 value 273.837510
## iter  40 value 272.585669
## iter  50 value 271.799251
## iter  60 value 270.608257
## iter  70 value 270.579744
## iter  80 value 270.377924
## iter  90 value 270.253583
## iter 100 value 270.043452
## final  value 270.043452 
## stopped after 100 iterations
## # weights:  72 (51 variable)
## initial  value 715.327890 
## iter  10 value 392.005795
## iter  20 value 283.756538
## iter  30 value 272.605766
## iter  40 value 271.271072
## iter  50 value 267.632938
## iter  60 value 267.048400
## iter  70 value 266.962960
## iter  80 value 266.717552
## iter  90 value 265.803091
## iter 100 value 265.698662
## final  value 265.698662 
## stopped after 100 iterations
## # weights:  72 (51 variable)
## initial  value 715.327890 
## iter  10 value 392.008683
## iter  20 value 293.296420
## iter  30 value 285.689908
## iter  40 value 285.018282
## final  value 284.928945 
## converged
## # weights:  72 (51 variable)
## initial  value 715.327890 
## iter  10 value 392.005798
## iter  20 value 283.774483
## iter  30 value 272.643939
## iter  40 value 271.366861
## iter  50 value 268.845499
## iter  60 value 268.568973
## iter  70 value 268.487423
## iter  80 value 268.228353
## iter  90 value 268.120271
## iter 100 value 268.005604
## final  value 268.005604 
## stopped after 100 iterations
## # weights:  72 (51 variable)
## initial  value 1072.991836 
## iter  10 value 555.683162
## iter  20 value 426.452643
## iter  30 value 416.550856
## iter  40 value 413.447536
## iter  50 value 413.072117
## iter  60 value 412.305407
## iter  70 value 412.063181
## iter  80 value 411.540277
## iter  90 value 411.525503
## iter 100 value 411.252516
## final  value 411.252516 
## stopped after 100 iterations
DryBean_TDA_KDE_5.40.5_n5_LrFit0
## Penalized Multinomial Regression 
## 
## 774 samples
##  16 predictor
##   4 classes: 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 515, 517, 516 
## Resampling results across tuning parameters:
## 
##   decay  Accuracy   Kappa    
##   0e+00  0.7338124  0.5191699
##   1e-04  0.7364364  0.5212859
##   1e-01  0.7261053  0.4909906
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was decay = 1e-04.
DryBean_TDA_KDE_5.40.5_n5_LrFit0$resample
##    Accuracy     Kappa Resample
## 1 0.7315175 0.5164035    Fold2
## 2 0.7297297 0.5111495    Fold1
## 3 0.7480620 0.5363048    Fold3
nb_tda_kde_5.40.5_n5_lr_fit_re<-DryBean_TDA_KDE_5.40.5_n5_LrFit0$resample[1]

summary(DryBean_TDA_KDE_5.40.5_n5_LrFit0)
## Call:
## nnet::multinom(formula = .outcome ~ ., data = dat, decay = param$decay, 
##     family = "binomial")
## 
## Coefficients:
##        (Intercept)         Area   Perimeter MajorAxisLength MinorAxisLength
## HOROZ   0.02531359 -0.077793336 -0.33133452       0.1140418       -8.000236
## SEKER  -8.12689964  0.021407043  0.26570689      -3.7240218       -4.867661
## SIRA  -16.68229175 -0.001321159 -0.05519992       0.1514487        0.747400
##       AspectRation Eccentricity   ConvexArea EquivDiameter    Extent
## HOROZ    0.5499971   -0.1842233  0.078741126     6.8849288 31.581368
## SEKER  -19.5974641  -37.7644759 -0.021620828     8.0150668 -0.723981
## SIRA   -31.2205936   50.2989311 -0.002785746     0.9404719 12.680554
##          Solidity    roundness Compactness ShapeFactor1  ShapeFactor2
## HOROZ  0.03765175  -0.04419988   0.0691424  0.000515291  0.0004973407
## SEKER -5.87152976  -0.71956338   1.9452989 -0.260678569  0.0883285824
## SIRA  -8.86067944 -78.24197406 -32.9979781 -0.206788247 -0.3889296212
##       ShapeFactor3 ShapeFactor4
## HOROZ    0.1570804     0.217960
## SEKER   11.6490272    -4.415946
## SIRA   -51.6522937   -28.500255
## 
## Std. Errors:
##        (Intercept)         Area    Perimeter MajorAxisLength MinorAxisLength
## HOROZ 1.403890e-09 1.954579e-05 1.180756e-06    2.793500e-07    1.168078e-07
## SEKER 8.842265e-07 2.663973e-03 6.266539e-04    1.094858e-04    8.361600e-05
## SIRA  1.125842e-05 1.362706e-03 5.240345e-03    2.593730e-03    2.773576e-04
##       AspectRation Eccentricity   ConvexArea EquivDiameter       Extent
## HOROZ 3.297510e-09 1.434706e-09 1.957882e-05  1.838605e-07 5.290888e-09
## SEKER 1.186495e-06 5.754347e-07 2.626208e-03  9.488256e-05 4.054459e-07
## SIRA  2.831257e-05 1.367455e-05 1.397753e-03  1.239471e-03 7.866055e-06
##           Solidity    roundness  Compactness ShapeFactor1 ShapeFactor2
## HOROZ 1.513840e-09 8.135882e-10 1.040036e-09 1.586784e-11 3.434912e-12
## SEKER 8.209745e-07 9.464321e-07 7.633997e-07 8.958120e-09 3.045089e-09
## SIRA  1.116065e-05 7.359997e-06 5.899797e-06 1.468320e-07 9.131187e-09
##       ShapeFactor3 ShapeFactor4
## HOROZ 8.122022e-10 1.703429e-09
## SEKER 6.607627e-07 8.775611e-07
## SIRA  2.062744e-06 1.121153e-05
## 
## Residual Deviance: 822.505 
## AIC: 924.505
vip(DryBean_TDA_KDE_5.40.5_n5_LrFit0,50) + ggtitle("DryBean_TDA_KDE_5.40.5_n5_Lr1Fit TDA-Assited LR")

# Predict outcome using DryBean_TDA_KDE_5.40.5_n5_LrFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.40.5_n5_LrFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.40.5_n5_db_lr_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length

## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
nb_tda_kde_5.40.5_n5_db_lr_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     0    0
##   DERMASON       11      1    5      876     0    20  153
##   HOROZ         218    122  473      157   578     4  341
##   SEKER         151     33   10       10     0   577   18
##   SIRA           16      0    1       20     0     7  278
## 
## Overall Statistics
##                                           
##                Accuracy : 0.5659          
##                  95% CI : (0.5506, 0.5812)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.4718          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000      0.0000          0.8241
## Specificity                  1.00000       1.00000      1.0000          0.9370
## Pos Pred Value                   NaN           NaN         NaN          0.8218
## Neg Pred Value               0.90294       0.96176      0.8801          0.9380
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.00000       0.00000      0.0000          0.2147
## Detection Prevalence         0.00000       0.00000      0.0000          0.2613
## Balanced Accuracy            0.50000       0.50000      0.5000          0.8806
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                1.0000       0.9490     0.35190
## Specificity                0.6245       0.9361     0.98663
## Pos Pred Value             0.3053       0.7222     0.86335
## Neg Pred Value             1.0000       0.9906     0.86376
## Prevalence                 0.1417       0.1490     0.19363
## Detection Rate             0.1417       0.1414     0.06814
## Detection Prevalence       0.4640       0.1958     0.07892
## Balanced Accuracy          0.8123       0.9425     0.66926
nb_tda_kde_5.40.5_n5_db_lr_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA        0      0    0        0     0     0    0
##   BOMBAY          0      0    0        0     0     0    0
##   CALI            0      0    0        0     0     0    0
##   DERMASON       11      1    5      876     0    20  153
##   HOROZ         218    122  473      157   578     4  341
##   SEKER         151     33   10       10     0   577   18
##   SIRA           16      0    1       20     0     7  278
## 
## Overall Statistics
##                                           
##                Accuracy : 0.5659          
##                  95% CI : (0.5506, 0.5812)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.4718          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.00000       0.00000      0.0000          0.8241
## Specificity                  1.00000       1.00000      1.0000          0.9370
## Pos Pred Value                   NaN           NaN         NaN          0.8218
## Neg Pred Value               0.90294       0.96176      0.8801          0.9380
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.00000       0.00000      0.0000          0.2147
## Detection Prevalence         0.00000       0.00000      0.0000          0.2613
## Balanced Accuracy            0.50000       0.50000      0.5000          0.8806
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                1.0000       0.9490     0.35190
## Specificity                0.6245       0.9361     0.98663
## Pos Pred Value             0.3053       0.7222     0.86335
## Neg Pred Value             1.0000       0.9906     0.86376
## Prevalence                 0.1417       0.1490     0.19363
## Detection Rate             0.1417       0.1414     0.06814
## Detection Prevalence       0.4640       0.1958     0.07892
## Balanced Accuracy          0.8123       0.9425     0.66926
nb_tda_kde_5.40.5_n5_db_lr_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.5659314      0.4717652      0.5505579      0.5812104      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
nb_tda_kde_5.40.5_n5_db_lr_cf0_ov_acc<-nb_tda_kde_5.40.5_n5_db_lr_cf0$overall[1]
nb_tda_kde_5.40.5_n5_db_lr_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.0000000   1.0000000            NaN      0.9029412        NA
## Class: BOMBAY     0.0000000   1.0000000            NaN      0.9617647        NA
## Class: CALI       0.0000000   1.0000000            NaN      0.8801471        NA
## Class: DERMASON   0.8240828   0.9370235      0.8217636      0.9379562 0.8217636
## Class: HOROZ      1.0000000   0.6245003      0.3053354      1.0000000 0.3053354
## Class: SEKER      0.9490132   0.9360599      0.7221527      0.9905517 0.7221527
## Class: SIRA       0.3518987   0.9866261      0.8633540      0.8637573 0.8633540
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.0000000        NA 0.09705882     0.00000000
## Class: BOMBAY   0.0000000        NA 0.03823529     0.00000000
## Class: CALI     0.0000000        NA 0.11985294     0.00000000
## Class: DERMASON 0.8240828 0.8229216 0.26053922     0.21470588
## Class: HOROZ    1.0000000 0.4678268 0.14166667     0.14166667
## Class: SEKER    0.9490132 0.8201848 0.14901961     0.14142157
## Class: SIRA     0.3518987 0.5000000 0.19362745     0.06813725
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA           0.00000000         0.5000000
## Class: BOMBAY             0.00000000         0.5000000
## Class: CALI               0.00000000         0.5000000
## Class: DERMASON           0.26127451         0.8805532
## Class: HOROZ              0.46397059         0.8122501
## Class: SEKER              0.19583333         0.9425365
## Class: SIRA               0.07892157         0.6692624
nb_tda_kde_5.40.5_n5_db_lr_cf0_pre_rec_f1<-nb_tda_kde_5.40.5_n5_db_lr_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_kde_5.40.5_lr_n5_3_fold<-(db_lr_fit_re - nb_tda_kde_5.40.5_n5_lr_fit_re)
diff_drybean_tda_kde_5.40.5_lr_n5_3_fold
##    Accuracy
## 1 0.1995278
## 2 0.1944125
## 3 0.1754748
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_lr.n5_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_lr_n5_3_fold),-0.01,0.01)
bst_tda_kde_5.40.5_lr.n5_3_fold
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_lr.n5_3_fold_odds.left<-bst_tda_kde_5.40.5_lr.n5_3_fold$probLeft/bst_tda_kde_5.40.5_lr.n5_3_fold$probRight
bst_tda_kde_5.40.5_lr.n5_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_lr.n5_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_lr_n5_3_fold),-0.01,0.01)
bsr_tda_kde_5.40.5_lr.n5_3_fold
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.0085
## 
## $winRight
## [1] 0.9915
# Bayesian Correlated Test

bct_tda_kde_5.40.5_lr.n5_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_lr_n5_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_lr.n5_3_fold
## $left
## [1] 0.0008913453
## 
## $rope
## [1] 0.0002086293
## 
## $right
## [1] 0.9989
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.40.5_lr_n5_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_kde_5.40.5_lr.n5_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_lr_n5_3_fold))
#bf_tda_kde_5.40.5_lr.n5_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.40.5_lr_n5_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_kde_5.40.5_lr_n5_3_fold)
## t = 25.945, df = 2, p-value = 0.001482
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  0.1583281 0.2212820
## sample estimates:
## mean of x 
## 0.1898051
### Test set diff
diff_drybean_tda_kde_5.40.5_lr.n5_test<-(db_svm_cf_ov_acc - nb_tda_kde_5.40.5_n5_db_lr_cf0_ov_acc)
diff_drybean_tda_kde_5.40.5_lr.n5_test
##  Accuracy 
## 0.3615196
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_lr.n5_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_lr.n5_test),-0.01,0.01)
bst_tda_kde_5.40.5_lr.n5_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_lr.n5_test_odds.left<-bst_tda_kde_5.40.5_lr.n5_test$probLeft/bst_tda_kde_5.40.5_lr.n5_test$probRight
bst_tda_kde_5.40.5_lr.n5_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_lr.n5_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_lr.n5_test),-0.01,0.01)
bsr_tda_kde_5.40.5_lr.n5_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1567667
## 
## $winRight
## [1] 0.8432333
# Bayesian Correlated Test

bct_tda_kde_5.40.5_lr.n5_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_lr.n5_test),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_lr.n5_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_lr.n5_test)))

#BayesFactor
#bf_tda_kde_5.40.5_lr.n5_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_lr.n5_test)) #bf_tda_pca_5.40.5_lr.n5_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_lr.n5_test))


#naiveBayes 
dryBeanNbFit <- train(as.factor(Class) ~ ., data = Dry_Bean_DatasetTrain, 
                method = 'nb', 
                trControl = fitControl,
                metric='Accuracy')
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 93
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 403
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 438
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 492
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 508
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 513
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 520
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 803
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 869
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 873
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 877
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 879
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 896
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 916
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1563
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2547
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 93
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 438
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 492
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 508
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 513
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 516
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 520
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 559
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 803
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 839
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 859
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 869
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 873
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 877
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 879
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 896
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 916
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1292
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1524
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1563
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1601
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1812
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2435
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2495
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2547
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3033
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 317
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 456
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 458
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 462
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 473
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 474
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 515
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 530
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 567
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 840
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 862
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 875
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 877
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 891
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 904
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 906
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1048
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1050
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1307
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1405
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1431
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1526
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1532
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1562
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1569
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2314
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2441
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2711
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 292
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 317
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 399
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 443
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 455
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 456
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 458
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 461
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 462
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 473
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 515
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 530
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 567
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 589
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 629
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 744
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 769
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 817
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 840
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 862
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 875
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 876
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 877
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 891
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 904
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 906
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1050
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1307
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1405
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1431
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1526
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1532
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1541
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1562
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1569
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2314
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2422
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2441
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2509
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2592
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2711
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 400
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 473
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 562
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 844
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 890
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 894
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 895
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 897
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 904
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 912
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1302
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1531
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1540
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1577
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1579
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1858
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2416
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2510
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2557
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2598
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2618
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3012
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 400
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 473
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 474
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 562
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 592
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 741
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 844
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 890
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 894
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 895
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 896
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 897
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 904
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 912
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1302
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1326
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1520
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1540
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1577
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1579
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1739
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1858
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2510
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2526
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2557
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2618
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3012
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3026
dryBeanNbFit
## Naive Bayes 
## 
## 9531 samples
##   16 predictor
##    7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 6355, 6353, 6354 
## Resampling results across tuning parameters:
## 
##   usekernel  Accuracy   Kappa    
##   FALSE      0.9000109  0.8792485
##    TRUE      0.9028444  0.8825798
## 
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
##  parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = TRUE and adjust
##  = 1.
dryBeanNbFit$resample
##    Accuracy     Kappa Resample
## 1 0.9080605 0.8888929    Fold1
## 2 0.8977344 0.8763074    Fold2
## 3 0.9027384 0.8825389    Fold3
db_nb_fit_re<-dryBeanNbFit$resample[1]

summary(dryBeanNbFit)
##             Length Class      Mode     
## apriori      7     table      numeric  
## tables      16     -none-     list     
## levels       7     -none-     character
## call         6     -none-     call     
## x           16     data.frame list     
## usekernel    1     -none-     logical  
## varnames    16     -none-     character
## xNames      16     -none-     character
## problemType  1     -none-     character
## tuneValue    3     data.frame list     
## obsLevels    7     -none-     character
## param        0     -none-     list
#varImp (dryBeanNbFit)



# Predict outcome using model from training data based on testing data
predictions <- predict(dryBeanNbFit, newdata= Dry_Bean_DatasetTest)
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 89
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 504
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 514
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 568
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 578
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 593
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 596
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 598
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 601
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 606
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 608
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 618
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 628
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 642
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 943
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 977
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 992
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 993
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 994
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1000
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1001
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1002
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1009
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1013
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1016
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1165
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1525
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1571
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1615
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1692
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2206
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2223
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2321
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2972
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3024
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3025
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3043
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3059
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3061
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3083
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3085
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3091
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3092
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3755
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3961
# Create confusion matrix to assess model fit/performance on test data
db_nb_cf<-confusionMatrix(data=predictions, as.factor(Dry_Bean_DatasetTest$Class))
db_nb_cf
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      299      0   38        0     1     5    6
##   BOMBAY          1    156    0        0     0     0    0
##   CALI           68      0  437        0    13     0    0
##   DERMASON        0      0    0      951     4     9   68
##   HOROZ           4      0   11        1   550     0   18
##   SEKER           3      0    1       22     0   567    9
##   SIRA           21      0    2       89    10    27  689
## 
## Overall Statistics
##                                           
##                Accuracy : 0.8944          
##                  95% CI : (0.8845, 0.9036)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.8723          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.75505       1.00000      0.8937          0.8946
## Specificity                  0.98643       0.99975      0.9774          0.9732
## Pos Pred Value               0.85673       0.99363      0.8436          0.9215
## Neg Pred Value               0.97400       1.00000      0.9854          0.9633
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.07328       0.03824      0.1071          0.2331
## Detection Prevalence         0.08554       0.03848      0.1270          0.2529
## Balanced Accuracy            0.87074       0.99987      0.9356          0.9339
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9516       0.9326      0.8722
## Specificity                0.9903       0.9899      0.9547
## Pos Pred Value             0.9418       0.9419      0.8222
## Neg Pred Value             0.9920       0.9882      0.9688
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1348       0.1390      0.1689
## Detection Prevalence       0.1431       0.1475      0.2054
## Balanced Accuracy          0.9709       0.9612      0.9134
db_nb_cf$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.8943627      0.8722759      0.8845246      0.9036320      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
db_nb_cf_ov_acc<-db_nb_cf$overall[1]
db_nb_cf$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.7550505   0.9864278      0.8567335      0.9740016 0.8567335
## Class: BOMBAY     1.0000000   0.9997452      0.9936306      1.0000000 0.9936306
## Class: CALI       0.8936605   0.9774436      0.8436293      0.9854015 0.8436293
## Class: DERMASON   0.8946378   0.9731521      0.9215116      0.9632546 0.9215116
## Class: HOROZ      0.9515571   0.9902913      0.9417808      0.9919908 0.9417808
## Class: SEKER      0.9325658   0.9899194      0.9418605      0.9882116 0.9418605
## Class: SIRA       0.8721519   0.9547112      0.8221957      0.9688464 0.8221957
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.7550505 0.8026846 0.09705882     0.07328431
## Class: BOMBAY   1.0000000 0.9968051 0.03823529     0.03823529
## Class: CALI     0.8936605 0.8679245 0.11985294     0.10710784
## Class: DERMASON 0.8946378 0.9078759 0.26053922     0.23308824
## Class: HOROZ    0.9515571 0.9466437 0.14166667     0.13480392
## Class: SEKER    0.9325658 0.9371901 0.14901961     0.13897059
## Class: SIRA     0.8721519 0.8464373 0.19362745     0.16887255
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA           0.08553922         0.8707392
## Class: BOMBAY             0.03848039         0.9998726
## Class: CALI               0.12696078         0.9355521
## Class: DERMASON           0.25294118         0.9338950
## Class: HOROZ              0.14313725         0.9709242
## Class: SEKER              0.14754902         0.9612426
## Class: SIRA               0.20539216         0.9134316
db_nb_cf_pre_rec_f1<-db_nb_cf$byClass[5:7]


##With TDA PCA filter 5 intervals, 50% overlap, 5 bins 
##Node1

DryBean_TDA_PC_5.40.5_n1_NbFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.40.5.n1.vec, 
                method = 'nb', 
                trControl = fitControl,
                metric='Accuracy')
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 559
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1152
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2244
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 414
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1152
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1329
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1422
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1906
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2244
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 78
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 437
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 616
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 637
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1223
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1329
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1417
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1556
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1586
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2084
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2092
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 78
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 437
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 445
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 616
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1221
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1223
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1329
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1417
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1556
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1579
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1586
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1613
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1815
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1968
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2084
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2092
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 90
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1408
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1504
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1588
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2102
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 90
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 841
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1390
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1408
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1506
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1624
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1816
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2248
DryBean_TDA_PC_5.40.5_n1_NbFit0
## Naive Bayes 
## 
## 6835 samples
##   16 predictor
##    6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 4555, 4557, 4558 
## Resampling results across tuning parameters:
## 
##   usekernel  Accuracy   Kappa    
##   FALSE      0.8307209  0.7426375
##    TRUE      0.8348213  0.7472254
## 
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
##  parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = TRUE and adjust
##  = 1.
DryBean_TDA_PC_5.40.5_n1_NbFit0$resample
##    Accuracy     Kappa Resample
## 1 0.8372807 0.7494038    Fold1
## 2 0.8235294 0.7320441    Fold2
## 3 0.8436539 0.7602284    Fold3
db_tda_pc_5.40.5_n1_nb_fit_re<-DryBean_TDA_PC_5.40.5_n1_NbFit0$resample[1]

summary(DryBean_TDA_PC_5.40.5_n1_NbFit0)
##             Length Class      Mode     
## apriori      6     table      numeric  
## tables      16     -none-     list     
## levels       6     -none-     character
## call         6     -none-     call     
## x           16     data.frame list     
## usekernel    1     -none-     logical  
## varnames    16     -none-     character
## xNames      16     -none-     character
## problemType  1     -none-     character
## tuneValue    3     data.frame list     
## obsLevels    6     -none-     character
## param        0     -none-     list
# Predict outcome using DryBean_TDA_PC_5.40.5_n1_NbFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.40.5_n1_NbFit0, newdata= Dry_Bean_DatasetTest)
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 89
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 603
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 605
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 612
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 626
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 629
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 635
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 637
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 639
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 640
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 679
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 681
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 684
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 688
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 692
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 696
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 697
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 698
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 699
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 701
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 703
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 704
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 705
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 708
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 711
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 713
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 738
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 739
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 741
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 744
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 754
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 755
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 758
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 760
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 768
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 769
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 797
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 801
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 803
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 806
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 809
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 812
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 814
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 815
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 816
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 817
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 818
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 819
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 825
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 830
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 837
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 839
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 840
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 841
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 842
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 843
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 844
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 845
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 846
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 847
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 848
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 850
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 851
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 852
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 853
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 854
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 855
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 856
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 857
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 858
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 859
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 860
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 861
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 862
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 863
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 864
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 865
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 866
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 868
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 869
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 871
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 872
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 873
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 874
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 875
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 876
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 877
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 878
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 879
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 880
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 883
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 885
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 887
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 888
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 889
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 890
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 891
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 892
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 894
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 895
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 896
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 897
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 904
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 906
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 909
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 910
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 911
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 912
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 914
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 915
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 916
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 929
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 938
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 940
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 943
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 954
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 956
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 957
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 958
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 959
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 960
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 963
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 964
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 965
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 966
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 967
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 968
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 969
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 972
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 974
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 975
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 977
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 978
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 983
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 984
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 985
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 986
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 988
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 990
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 991
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 992
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 993
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 994
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 997
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1000
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1001
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1002
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1009
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1012
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1013
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1014
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1015
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1016
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1017
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1024
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1025
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1028
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1029
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1030
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1031
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1033
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1034
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1037
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1042
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1043
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1044
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1046
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1048
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1050
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1051
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1052
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1053
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1055
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1056
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1057
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1058
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1059
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1060
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1061
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1062
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1065
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1071
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1072
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1074
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1075
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1077
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1080
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1081
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1082
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1083
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1084
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1085
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1087
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1088
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1089
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1091
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1092
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1093
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1094
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1095
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1096
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1097
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1098
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1099
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1100
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1101
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1102
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1104
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1122
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1124
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1131
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1132
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1152
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1165
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1166
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1167
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1175
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1176
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1181
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1182
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1189
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1190
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1195
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1206
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1215
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1216
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1219
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1221
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1223
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1226
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1235
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1236
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1238
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1242
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1243
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1244
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1246
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1251
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1259
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1292
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1302
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1303
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1307
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1312
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1314
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1315
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1317
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1318
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1319
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1320
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1321
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1322
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1323
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1324
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1326
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1327
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1328
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1329
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1330
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1331
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1332
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1333
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1334
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1335
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1337
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1340
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1342
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1345
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1347
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1361
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1387
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1390
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1391
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1397
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1399
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1400
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1401
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1402
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1403
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1405
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1406
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1407
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1408
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1409
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1410
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1411
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1412
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1414
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1416
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1417
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1419
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1420
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1422
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1425
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1427
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1429
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1431
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1434
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1435
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1436
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1437
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1438
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1439
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1441
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1442
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1443
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1444
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1445
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1447
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1448
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1449
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1450
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1452
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1455
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1456
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1457
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1458
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1461
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1462
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1465
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1473
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1474
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1476
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1487
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1490
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1491
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1492
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1495
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1496
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1499
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1501
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1502
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1503
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1504
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1506
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1507
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1508
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1509
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1510
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1511
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1512
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1513
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1514
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1515
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1516
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1518
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1519
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1520
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1522
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1524
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1525
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1526
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1528
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1529
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1530
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1531
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1532
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1533
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1534
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1535
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1536
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1537
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1538
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1539
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1540
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1541
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1542
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1543
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1544
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1545
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1546
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1547
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1550
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1551
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1552
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1553
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1554
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1555
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1556
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1557
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1558
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1559
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1561
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1562
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1563
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1564
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1565
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1567
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1568
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1569
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1570
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1571
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1572
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1574
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1576
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1577
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1578
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1579
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1583
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1584
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1585
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1586
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1588
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1589
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1590
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1591
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1592
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1593
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1595
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1596
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1598
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1599
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1601
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1603
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1605
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1606
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1608
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1609
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1611
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1612
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1613
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1615
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1616
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1618
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1619
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1624
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1626
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1628
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1629
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1630
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1633
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1635
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1637
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1639
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1640
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1642
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1679
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1681
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1684
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1688
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1692
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1696
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1697
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1698
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1699
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1701
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1703
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1704
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1705
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1708
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1711
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1713
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1738
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1739
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1741
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1744
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1754
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1755
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1758
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1760
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1768
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1769
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1797
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1801
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1803
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1806
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1809
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1812
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1814
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1815
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1816
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1817
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1818
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1819
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1825
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1830
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1837
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1839
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1840
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1841
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1842
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1843
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1844
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1845
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1846
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1847
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1848
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1850
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1851
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1852
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1853
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1854
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1855
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1856
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1857
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1858
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1859
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1860
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1861
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1862
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1863
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1864
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1865
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1866
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1868
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1869
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1871
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1872
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1873
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1874
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1875
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1876
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1877
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1878
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1879
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1880
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1883
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1885
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1887
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1888
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1889
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1890
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1891
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1892
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1894
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1895
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1896
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1897
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1904
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1906
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1909
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1910
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1911
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1912
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1914
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1915
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1916
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1929
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1938
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1940
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1943
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1954
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1956
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1957
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1958
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1959
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1960
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1963
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1964
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1965
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1966
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1967
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1968
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1969
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1972
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1974
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1975
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1977
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1978
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1983
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1984
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1985
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1986
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1988
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1990
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1991
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1992
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1993
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1994
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1997
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2000
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2001
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2002
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2009
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2012
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2013
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2014
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2015
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2016
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2017
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2024
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2025
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2028
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2029
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2030
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2031
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2033
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2034
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2037
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2042
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2043
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2044
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2046
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2048
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2050
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2051
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2052
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2053
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2055
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2056
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2057
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2058
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2059
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2060
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2061
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2062
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2065
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2071
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2072
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2074
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2075
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2077
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2080
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2081
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2082
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2083
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2084
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2085
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2087
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2088
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2089
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2091
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2092
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2093
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2094
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2095
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2096
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2097
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2098
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2099
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2100
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2101
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2102
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2104
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2122
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2124
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2131
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2132
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2152
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2165
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2166
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2167
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2175
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2176
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2181
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2182
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2189
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2190
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2195
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2206
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2215
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2216
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2219
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2221
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2223
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2226
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2307
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2321
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2412
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2431
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2435
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2447
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2458
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2509
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2510
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2571
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2601
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2619
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2698
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2803
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2814
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2825
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2841
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2848
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2850
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2852
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2853
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2855
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2856
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2857
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2858
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2861
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2862
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2863
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2871
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2874
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2876
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2878
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2885
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2887
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2888
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2889
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2891
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2895
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2906
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2909
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2911
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2914
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2915
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2929
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2940
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2943
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2954
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2956
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2957
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2958
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2959
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2960
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2963
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2964
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2965
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2966
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2967
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2969
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2972
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2974
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2977
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2978
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2983
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2984
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2985
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2986
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2988
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2990
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2991
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2992
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2993
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2994
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2997
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3000
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3001
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3002
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3009
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3012
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3013
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3014
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3015
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3016
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3017
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3024
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3043
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3083
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3085
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3091
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3092
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4053
# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.40.5_n1_db_nb_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in confusionMatrix.default(data = pred0,
## as.factor(Dry_Bean_DatasetTest$Class)): Levels are not in the same order for
## reference and data. Refactoring data to match.
db_tda_pc_5.40.5_n1_db_nb_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      254      0   68        0   279    29  100
##   BOMBAY          0      0    0        0     0     0    0
##   CALI           42     38  185        0   239    22   14
##   DERMASON        0      0    0      836     2     8   29
##   HOROZ          97    118  234       71    56     1   65
##   SEKER           0      0    0       20     0   531    4
##   SIRA            3      0    2      136     2    17  578
## 
## Overall Statistics
##                                           
##                Accuracy : 0.598           
##                  95% CI : (0.5828, 0.6131)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.5176          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.64141       0.00000     0.37832          0.7865
## Specificity                  0.87079       1.00000     0.90114          0.9871
## Pos Pred Value               0.34795           NaN     0.34259          0.9554
## Neg Pred Value               0.95761       0.96176     0.91412          0.9292
## Prevalence                   0.09706       0.03824     0.11985          0.2605
## Detection Rate               0.06225       0.00000     0.04534          0.2049
## Detection Prevalence         0.17892       0.00000     0.13235          0.2145
## Balanced Accuracy            0.75610       0.50000     0.63973          0.8868
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity               0.09689       0.8734      0.7316
## Specificity               0.83267       0.9931      0.9514
## Pos Pred Value            0.08723       0.9568      0.7832
## Neg Pred Value            0.84817       0.9782      0.9366
## Prevalence                0.14167       0.1490      0.1936
## Detection Rate            0.01373       0.1301      0.1417
## Detection Prevalence      0.15735       0.1360      0.1809
## Balanced Accuracy         0.46478       0.9332      0.8415
db_tda_pc_5.40.5_n1_db_nb_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      254      0   68        0   279    29  100
##   BOMBAY          0      0    0        0     0     0    0
##   CALI           42     38  185        0   239    22   14
##   DERMASON        0      0    0      836     2     8   29
##   HOROZ          97    118  234       71    56     1   65
##   SEKER           0      0    0       20     0   531    4
##   SIRA            3      0    2      136     2    17  578
## 
## Overall Statistics
##                                           
##                Accuracy : 0.598           
##                  95% CI : (0.5828, 0.6131)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.5176          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.64141       0.00000     0.37832          0.7865
## Specificity                  0.87079       1.00000     0.90114          0.9871
## Pos Pred Value               0.34795           NaN     0.34259          0.9554
## Neg Pred Value               0.95761       0.96176     0.91412          0.9292
## Prevalence                   0.09706       0.03824     0.11985          0.2605
## Detection Rate               0.06225       0.00000     0.04534          0.2049
## Detection Prevalence         0.17892       0.00000     0.13235          0.2145
## Balanced Accuracy            0.75610       0.50000     0.63973          0.8868
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity               0.09689       0.8734      0.7316
## Specificity               0.83267       0.9931      0.9514
## Pos Pred Value            0.08723       0.9568      0.7832
## Neg Pred Value            0.84817       0.9782      0.9366
## Prevalence                0.14167       0.1490      0.1936
## Detection Rate            0.01373       0.1301      0.1417
## Detection Prevalence      0.15735       0.1360      0.1809
## Balanced Accuracy         0.46478       0.9332      0.8415
db_tda_pc_5.40.5_n1_db_nb_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.5980392      0.5176331      0.5828065      0.6131314      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
db_tda_pc_5.40.5_n1_db_nb_cf0_ov_acc<-db_tda_pc_5.40.5_n1_db_nb_cf0$overall[1]
db_tda_pc_5.40.5_n1_db_nb_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value
## Class: BARBUNYA  0.64141414   0.8707926     0.34794521      0.9576119
## Class: BOMBAY    0.00000000   1.0000000            NaN      0.9617647
## Class: CALI      0.37832311   0.9011417     0.34259259      0.9141243
## Class: DERMASON  0.78645343   0.9870733     0.95542857      0.9291732
## Class: HOROZ     0.09688581   0.8326670     0.08722741      0.8481675
## Class: SEKER     0.87335526   0.9930876     0.95675676      0.9781560
## Class: SIRA      0.73164557   0.9513678     0.78319783      0.9365649
##                  Precision     Recall         F1 Prevalence Detection Rate
## Class: BARBUNYA 0.34794521 0.64141414 0.45115453 0.09705882     0.06225490
## Class: BOMBAY           NA 0.00000000         NA 0.03823529     0.00000000
## Class: CALI     0.34259259 0.37832311 0.35957240 0.11985294     0.04534314
## Class: DERMASON 0.95542857 0.78645343 0.86274510 0.26053922     0.20490196
## Class: HOROZ    0.08722741 0.09688581 0.09180328 0.14166667     0.01372549
## Class: SEKER    0.95675676 0.87335526 0.91315563 0.14901961     0.13014706
## Class: SIRA     0.78319783 0.73164557 0.75654450 0.19362745     0.14166667
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA            0.1789216         0.7561034
## Class: BOMBAY              0.0000000         0.5000000
## Class: CALI                0.1323529         0.6397324
## Class: DERMASON            0.2144608         0.8867633
## Class: HOROZ               0.1573529         0.4647764
## Class: SEKER               0.1360294         0.9332214
## Class: SIRA                0.1808824         0.8415067
db_tda_pc_5.40.5_n1_db_nb_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n1_db_nb_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_pca_5.40.5_nb_n1_3_fold<-(db_nb_fit_re - db_tda_pc_5.40.5_n1_nb_fit_re)
diff_drybean_tda_pca_5.40.5_nb_n1_3_fold
##     Accuracy
## 1 0.07077975
## 2 0.07420501
## 3 0.05908450
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_nb.n1_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_nb_n1_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_nb.n1_3_fold
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_nb.n1_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_nb.n1_3_fold$probLeft/bst_dbf_db_tda_pca_5.40.5_nb.n1_3_fold$probRight
bst_dbf_db_tda_pca_5.40.5_nb.n1_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_nb.n1_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_nb_n1_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_nb.n1_3_fold
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.007833333
## 
## $winRight
## [1] 0.9921667
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_nb.n1_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_nb_n1_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_nb.n1_3_fold
## $left
## [1] 0.002278856
## 
## $rope
## [1] 0.001819159
## 
## $right
## [1] 0.995902
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.40.5_nb_n1_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_nb.n1_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_nb_n1_3_fold))
#bf_tda_pca_5.40.5_nb.n1_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.40.5_nb_n1_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_pca_5.40.5_nb_n1_3_fold)
## t = 14.861, df = 2, p-value = 0.004498
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  0.04832827 0.08771791
## sample estimates:
##  mean of x 
## 0.06802309
### Test set diff
diff_drybean_tda_pca_5.40.5_nb.n1_test<-(db_nb_cf_ov_acc - db_tda_pc_5.40.5_n1_db_nb_cf0_ov_acc)
diff_drybean_tda_pca_5.40.5_nb.n1_test
##  Accuracy 
## 0.2963235
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_nb.n1_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_nb.n1_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_nb.n1_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_nb.n1_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_nb.n1_test$probLeft/bst_dbf_db_tda_pca_5.40.5_nb.n1_test$probRight
bst_dbf_db_tda_pca_5.40.5_nb.n1_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_nb.n1_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_nb.n1_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_nb.n1_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1601667
## 
## $winRight
## [1] 0.8398333
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_nb.n1_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_nb.n1_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_nb.n1_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_nb.n1_test)))

#BayesFactor
#bf_tda_pca_5.40.5_nb.n1_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_nb.n1_test)) #bf_tda_pca_5.40.5_nb.n1_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_nb.n1_test))

##Node2

DryBean_TDA_PC_5.40.5_n2_NbFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.40.5.n2.vec, 
                method = 'nb', 
                trControl = fitControl,
               metric='Accuracy')
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 81
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 443
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 474
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 965
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1062
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1072
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1865
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1972
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 176
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 189
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 215
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 443
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 474
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 965
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1072
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1409
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1972
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2014
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2511
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2538
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2551
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 167
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 407
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 697
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 801
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1082
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2089
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 167
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 242
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 292
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 335
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 391
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 407
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 436
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 499
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 504
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 697
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1046
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1074
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1082
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1967
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2089
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2595
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 462
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1975
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1856
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1875
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1959
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1975
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2573
DryBean_TDA_PC_5.40.5_n2_NbFit0
## Naive Bayes 
## 
## 8024 samples
##   16 predictor
##    6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 5350, 5349, 5349 
## Resampling results across tuning parameters:
## 
##   usekernel  Accuracy   Kappa    
##   FALSE      0.8541876  0.8118884
##    TRUE      0.8589226  0.8176145
## 
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
##  parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = TRUE and adjust
##  = 1.
DryBean_TDA_PC_5.40.5_n2_NbFit0$resample
##    Accuracy     Kappa Resample
## 1 0.8541511 0.8115802    Fold1
## 2 0.8624299 0.8221305    Fold2
## 3 0.8601869 0.8191326    Fold3
db_tda_pc_5.40.5_n2_nb_fit_re<-DryBean_TDA_PC_5.40.5_n2_NbFit0$resample[1]

summary(DryBean_TDA_PC_5.40.5_n2_NbFit0)
##             Length Class      Mode     
## apriori      6     table      numeric  
## tables      16     -none-     list     
## levels       6     -none-     character
## call         6     -none-     call     
## x           16     data.frame list     
## usekernel    1     -none-     logical  
## varnames    16     -none-     character
## xNames      16     -none-     character
## problemType  1     -none-     character
## tuneValue    3     data.frame list     
## obsLevels    6     -none-     character
## param        0     -none-     list
 #Predict outcome using DryBean_TDA_PC_5.40.5_n2_NbFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.40.5_n2_NbFit0, newdata= Dry_Bean_DatasetTest)
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 78
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 79
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 80
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 81
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 82
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 83
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 84
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 85
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 86
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 87
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 88
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 89
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 90
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 91
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 92
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 93
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 94
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 95
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 96
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 97
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 98
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 99
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 100
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 101
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 102
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 104
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 122
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 124
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 131
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 132
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 166
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 176
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 182
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 189
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 190
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 215
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 221
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 226
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 242
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 246
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 302
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 318
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 319
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 320
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 324
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 327
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 347
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 401
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 416
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 422
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 427
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 429
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 436
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 441
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 501
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 506
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 515
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 518
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 534
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 841
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 856
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 878
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 904
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 943
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 954
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 958
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 959
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 965
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 966
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 967
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 968
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 969
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 974
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 975
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 977
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 978
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 983
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 984
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 985
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 986
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 988
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 990
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 991
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 992
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 993
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 994
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 997
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1000
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1001
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1002
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1009
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1012
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1013
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1014
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1015
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1016
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1017
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1024
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1025
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1028
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1029
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1030
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1031
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1033
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1034
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1037
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1042
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1043
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1044
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1046
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1048
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1050
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1051
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1052
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1053
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1055
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1056
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1057
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1058
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1059
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1060
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1061
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1062
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1065
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1071
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1072
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1074
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1075
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1077
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1080
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1081
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1082
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1083
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1084
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1085
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1087
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1088
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1089
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1091
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1092
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1093
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1094
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1095
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1096
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1097
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1098
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1099
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1100
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1101
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1102
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1104
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1122
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1124
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1131
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1132
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1152
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1243
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1317
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1323
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1324
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1327
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1331
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1345
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1347
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1361
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1399
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1400
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1402
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1406
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1443
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1448
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1449
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1450
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1455
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1456
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1462
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1476
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1487
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1491
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1508
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1509
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1511
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1513
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1516
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1519
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1520
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1522
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1525
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1532
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1537
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1538
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1539
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1540
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1541
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1542
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1543
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1544
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1550
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1551
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1552
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1553
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1554
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1555
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1556
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1558
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1559
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1561
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1565
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1567
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1568
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1569
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1570
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1571
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1572
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1574
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1576
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1578
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1579
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1583
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1584
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1585
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1586
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1588
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1589
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1590
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1591
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1592
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1593
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1595
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1596
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1598
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1599
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1601
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1603
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1605
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1606
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1608
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1609
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1611
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1612
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1613
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1615
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1616
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1618
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1619
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1624
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1626
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1628
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1629
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1630
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1633
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1635
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1637
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1639
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1640
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1642
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2051
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2053
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2057
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2087
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2152
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2167
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2175
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2181
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2182
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2189
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2190
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2195
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2206
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2215
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2216
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2223
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2226
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3024
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3025
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3028
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3029
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3030
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3031
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3033
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3034
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3037
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3042
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3043
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3044
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3046
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3048
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3050
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3051
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3052
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3053
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3055
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3056
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3057
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3058
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3059
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3060
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3061
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3062
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3065
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3071
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3072
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3074
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3075
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3081
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3082
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3083
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3084
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3085
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3087
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3088
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3091
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3092
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3093
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3095
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3097
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3100
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3221
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3238
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3329
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3330
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3335
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3410
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3444
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3542
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3626
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3703
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3961
# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.40.5_n2_db_nb_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in confusionMatrix.default(data = pred0,
## as.factor(Dry_Bean_DatasetTest$Class)): Levels are not in the same order for
## reference and data. Refactoring data to match.
db_tda_pc_5.40.5_n2_db_nb_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      300      2   67        0     3     2    3
##   BOMBAY          0      0    0        0     0     0    0
##   CALI           68     80  412        0    14     0    6
##   DERMASON        0     64    0      985     5    52   89
##   HOROZ           4      3    7        1   551     0   19
##   SEKER           5      7    1       21     0   541   28
##   SIRA           19      0    2       56     5    13  645
## 
## Overall Statistics
##                                           
##                Accuracy : 0.8417          
##                  95% CI : (0.8301, 0.8527)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.807           
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.75758       0.00000      0.8425          0.9266
## Specificity                  0.97910       1.00000      0.9532          0.9304
## Pos Pred Value               0.79576           NaN      0.7103          0.8243
## Neg Pred Value               0.97408       0.96176      0.9780          0.9730
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.07353       0.00000      0.1010          0.2414
## Detection Prevalence         0.09240       0.00000      0.1422          0.2929
## Balanced Accuracy            0.86834       0.50000      0.8979          0.9285
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9533       0.8898      0.8165
## Specificity                0.9903       0.9821      0.9711
## Pos Pred Value             0.9419       0.8972      0.8716
## Neg Pred Value             0.9923       0.9807      0.9566
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1350       0.1326      0.1581
## Detection Prevalence       0.1434       0.1478      0.1814
## Balanced Accuracy          0.9718       0.9360      0.8938
db_tda_pc_5.40.5_n2_db_nb_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      300      2   67        0     3     2    3
##   BOMBAY          0      0    0        0     0     0    0
##   CALI           68     80  412        0    14     0    6
##   DERMASON        0     64    0      985     5    52   89
##   HOROZ           4      3    7        1   551     0   19
##   SEKER           5      7    1       21     0   541   28
##   SIRA           19      0    2       56     5    13  645
## 
## Overall Statistics
##                                           
##                Accuracy : 0.8417          
##                  95% CI : (0.8301, 0.8527)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.807           
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.75758       0.00000      0.8425          0.9266
## Specificity                  0.97910       1.00000      0.9532          0.9304
## Pos Pred Value               0.79576           NaN      0.7103          0.8243
## Neg Pred Value               0.97408       0.96176      0.9780          0.9730
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.07353       0.00000      0.1010          0.2414
## Detection Prevalence         0.09240       0.00000      0.1422          0.2929
## Balanced Accuracy            0.86834       0.50000      0.8979          0.9285
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9533       0.8898      0.8165
## Specificity                0.9903       0.9821      0.9711
## Pos Pred Value             0.9419       0.8972      0.8716
## Neg Pred Value             0.9923       0.9807      0.9566
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1350       0.1326      0.1581
## Detection Prevalence       0.1434       0.1478      0.1814
## Balanced Accuracy          0.9718       0.9360      0.8938
db_tda_pc_5.40.5_n2_db_nb_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.8416667      0.8069643      0.8300987      0.8527430      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
db_tda_pc_5.40.5_n2_db_nb_cf0_ov_acc<-db_tda_pc_5.40.5_n2_db_nb_cf0$overall[1]
db_tda_pc_5.40.5_n2_db_nb_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.7575758   0.9790988      0.7957560      0.9740751 0.7957560
## Class: BOMBAY     0.0000000   1.0000000            NaN      0.9617647        NA
## Class: CALI       0.8425358   0.9532164      0.7103448      0.9780000 0.7103448
## Class: DERMASON   0.9266228   0.9303944      0.8242678      0.9729636 0.8242678
## Class: HOROZ      0.9532872   0.9902913      0.9418803      0.9922747 0.9418803
## Class: SEKER      0.8898026   0.9821429      0.8971808      0.9807305 0.8971808
## Class: SIRA       0.8164557   0.9711246      0.8716216      0.9565868 0.8716216
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.7575758 0.7761966 0.09705882     0.07352941
## Class: BOMBAY   0.0000000        NA 0.03823529     0.00000000
## Class: CALI     0.8425358 0.7708138 0.11985294     0.10098039
## Class: DERMASON 0.9266228 0.8724535 0.26053922     0.24142157
## Class: HOROZ    0.9532872 0.9475494 0.14166667     0.13504902
## Class: SEKER    0.8898026 0.8934765 0.14901961     0.13259804
## Class: SIRA     0.8164557 0.8431373 0.19362745     0.15808824
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA           0.09240196         0.8683373
## Class: BOMBAY             0.00000000         0.5000000
## Class: CALI               0.14215686         0.8978761
## Class: DERMASON           0.29289216         0.9285086
## Class: HOROZ              0.14338235         0.9717892
## Class: SEKER              0.14779412         0.9359727
## Class: SIRA               0.18137255         0.8937902
db_tda_pc_5.40.5_n2_db_nb_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n2_db_nb_cf0$byClass[5:7]#

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_pca_5.40.5_nb_n2_3_fold<-(db_nb_fit_re - db_tda_pc_5.40.5_n2_nb_fit_re)
diff_drybean_tda_pca_5.40.5_nb_n2_3_fold
##     Accuracy
## 1 0.05390937
## 2 0.03530452
## 3 0.04255152
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_nb.n2_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_nb_n2_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_nb.n2_3_fold
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_nb.n2_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_nb.n2_3_fold$probLeft/bst_dbf_db_tda_pca_5.40.5_nb.n2_3_fold$probRight
bst_dbf_db_tda_pca_5.40.5_nb.n2_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_nb.n2_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_nb_n2_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_nb.n2_3_fold
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.0083
## 
## $winRight
## [1] 0.9917
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_nb.n2_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_nb_n2_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_nb.n2_3_fold
## $left
## [1] 0.006588867
## 
## $rope
## [1] 0.009575758
## 
## $right
## [1] 0.9838354
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.40.5_nb_n2_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_nb.n2_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_nb_n2_3_fold))
#bf_tda_pca_5.40.5_nb.n2_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.40.5_nb_n2_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_pca_5.40.5_nb_n2_3_fold)
## t = 8.1122, df = 2, p-value = 0.01486
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  0.02062602 0.06721758
## sample estimates:
## mean of x 
## 0.0439218
### Test set diff
diff_drybean_tda_pca_5.40.5_nb.n2_test<-(db_nb_cf_ov_acc - db_tda_pc_5.40.5_n2_db_nb_cf0_ov_acc)
diff_drybean_tda_pca_5.40.5_nb.n2_test
##   Accuracy 
## 0.05269608
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_nb.n2_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_nb.n2_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_nb.n2_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_nb.n2_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_nb.n2_test$probLeft/bst_dbf_db_tda_pca_5.40.5_nb.n2_test$probRight
bst_dbf_db_tda_pca_5.40.5_nb.n2_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_nb.n2_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_nb.n2_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_nb.n2_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1595333
## 
## $winRight
## [1] 0.8404667
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_nb.n2_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_nb.n2_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_nb.n2_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_nb.n2_test)))

#BayesFactor
#bf_tda_pca_5.40.5_nb.n2_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_nb.n2_test)) #bf_tda_pca_5.40.5_nb.n2_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_nb.n2_test))

##Node3

#DryBean_TDA_PC_5.40.5_n3_NbFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.40.5.n3.vec, 
#                method = 'nb', 
#                trControl = fitControl,
#                metric='Accuracy')

#DryBean_TDA_PC_5.40.5_n3_NbFit0
#DryBean_TDA_PC_5.40.5_n3_NbFit0$resample
#db_tda_pc_5.40.5_n3_nb_fit_re<-DryBean_TDA_PC_5.40.5_n3_NbFit0$resample[1]

#summary(DryBean_TDA_PC_5.40.5_n3_NbFit0)

 #Predict outcome using DryBean_TDA_PC_5.40.5_n3_NbFit0 from training data based on testing data
#pred0 <- predict(DryBean_TDA_PC_5.40.5_n3_NbFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
#db_tda_pc_5.40.5_n3_db_nb_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
#db_tda_pc_5.40.5_n3_db_nb_cf0
#db_tda_pc_5.40.5_n3_db_nb_cf0 
#db_tda_pc_5.40.5_n3_db_nb_cf0$overall
#db_tda_pc_5.40.5_n3_db_nb_cf0_ov_acc<-db_tda_pc_5.40.5_n3_db_nb_cf0$overall[1]
#db_tda_pc_5.40.5_n3_db_nb_cf0$byClass
#db_tda_pc_5.40.5_n3_db_nb_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n3_db_nb_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

#diff_drybean_tda_pca_5.40.5_nb_n3_3_fold<-(db_nb_fit_re - db_tda_pc_5.40.5_n3_nb_fit_re)
#diff_drybean_tda_pca_5.40.5_nb_n3_3_fold

## Bayesian Tests 3-fold diff

# Bayesian Sign Test

#bst_dbf_db_tda_pca_5.40.5_nb.n3_3_fold<-#BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_nb_n3_3_fold),-0.01,0.01)
#bst_dbf_db_tda_pca_5.40.5_nb.n3_3_fold

# Odds Left Bayesian Sign Test 

#bst_dbf_db_tda_pca_5.40.5_nb.n3_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_nb.n3_3_fold$probLeft/bst_dbf_db_tda_pca_5.40.5_nb.n3_3_fold$probRight
#bst_dbf_db_tda_pca_5.40.5_nb.n3_3_fold_odds.left

# Bayesian Signed Rank Test

#bsr_dbf_db_tda_pca_5.40.5_nb.n3_3_fold<-#BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_nb_n3_3_fold),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.40.5_nb.n3_3_fold

# Bayesian Correlated Test

#bct_dbf_db_tda_pca_5.40.5_nb.n3_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_nb_n3_3_fold),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.40.5_nb.n3_3_fold

# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_nb_n3_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_nb.n3_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_nb_n3_3_fold))
#bf_tda_pca_5.40.5_nb.n3_3_fold

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_nb_n3_3_fold))


### Test set diff
#diff_drybean_tda_pca_5.40.5_nb.n3_test<-(db_nb_cf_ov_acc - db_tda_pc_5.40.5_n3_db_nb_cf0_ov_acc)
#diff_drybean_tda_pca_5.40.5_nb.n3_test


## Bayesian Tests Test set diff

# Bayesian Sign Test

#bst_dbf_db_tda_pca_5.40.5_nb.n3_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_nb.n3_test),-0.01,0.01)
#bst_dbf_db_tda_pca_5.40.5_nb.n3_test

# Odds Left Bayesian Sign Test 

#bst_dbf_db_tda_pca_5.40.5_nb.n3_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_nb.n3_test$probLeft/bst_dbf_db_tda_pca_5.40.5_nb.n3_test$probRight
#bst_dbf_db_tda_pca_5.40.5_nb.n3_test_odds.left

# Bayesian Signed Rank Test

#bsr_dbf_db_tda_pca_5.40.5_nb.n2_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_nb.n2_test),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.40.5_nb.n2_test

# Bayesian Correlated Test

#bct_dbf_db_tda_pca_5.40.5_nb.n3_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_nb.n3_test),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.40.5_nb.n3_test

# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_nb.n3_test)))

#BayesFactor
#bf_tda_pca_5.40.5_nb.n3_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_nb.n3_test)) #bf_tda_pca_5.40.5_nb.n3_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_nb.n3_test))

##Node4

DryBean_TDA_PC_5.40.5_n4_NbFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.40.5.n4.vec, 
                method = 'nb', 
                trControl = fitControl,
                metric='Accuracy')
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 84
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 90
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 166
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 235
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 84
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 90
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 166
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 235
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 94
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 82
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 85
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 82
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 85
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 266
DryBean_TDA_PC_5.40.5_n4_NbFit0
## Naive Bayes 
## 
## 894 samples
##  16 predictor
##   4 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'HOROZ' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 595, 596, 597 
## Resampling results across tuning parameters:
## 
##   usekernel  Accuracy   Kappa    
##   FALSE      0.9742953  0.9585317
##    TRUE      0.9664727  0.9458031
## 
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
##  parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = FALSE and adjust
##  = 1.
DryBean_TDA_PC_5.40.5_n4_NbFit0$resample
##    Accuracy     Kappa Resample
## 1 0.9632107 0.9406650    Fold1
## 2 0.9765101 0.9620727    Fold2
## 3 0.9831650 0.9728574    Fold3
db_tda_pc_5.40.5_n4_nb_fit_re<-DryBean_TDA_PC_5.40.5_n4_NbFit0$resample[1]

summary(DryBean_TDA_PC_5.40.5_n4_NbFit0)
##             Length Class      Mode     
## apriori      4     table      numeric  
## tables      16     -none-     list     
## levels       4     -none-     character
## call         5     -none-     call     
## x           16     data.frame list     
## usekernel    1     -none-     logical  
## varnames    16     -none-     character
## xNames      16     -none-     character
## problemType  1     -none-     character
## tuneValue    3     data.frame list     
## obsLevels    4     -none-     character
## param        0     -none-     list
# Predict outcome using DryBean_TDA_PC_5.40.5_n4_NbFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.40.5_n4_NbFit0, newdata= Dry_Bean_DatasetTest)
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 78
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 79
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 80
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 81
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 82
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 83
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 84
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 85
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 86
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 87
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 88
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 89
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 90
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 91
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 92
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 93
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 94
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 95
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 96
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 97
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 98
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 99
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 100
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 101
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 102
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 104
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 122
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 124
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 131
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 132
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 152
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 165
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 166
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 167
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 175
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 176
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 181
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 182
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 189
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 190
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 195
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 206
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 215
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 216
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 219
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 221
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 223
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 226
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 235
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 236
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 238
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 242
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 243
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 244
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 246
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 251
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 259
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 292
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 302
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 303
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 307
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 312
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 314
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 315
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 317
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 318
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 319
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 320
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 321
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 322
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 323
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 324
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 326
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 327
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 328
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 329
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 330
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 331
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 332
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 333
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 334
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 335
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 337
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 340
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 342
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 345
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 347
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 361
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 387
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 390
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 391
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 397
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 399
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 400
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 401
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 402
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 403
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 405
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 406
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 407
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 408
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 409
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 410
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 411
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 412
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 414
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 416
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 417
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 419
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 420
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 422
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 425
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 427
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 429
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 431
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 434
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 435
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 436
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 437
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 438
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 439
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 441
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 442
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 443
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 444
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 445
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 447
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 448
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 449
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 450
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 452
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 455
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 456
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 457
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 458
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 461
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 462
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 465
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 473
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 474
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 476
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 487
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 490
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 491
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 492
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 495
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 496
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 499
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 501
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 502
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 503
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 504
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 506
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 507
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 508
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 509
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 510
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 511
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 512
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 513
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 514
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 515
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 516
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 518
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 519
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 520
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 522
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 524
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 525
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 526
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 528
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 529
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 530
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 531
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 532
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 533
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 534
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 535
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 536
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 537
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 538
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 539
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 540
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 541
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 542
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 543
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 544
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 545
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 546
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 547
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 550
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 551
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 552
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 553
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 554
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 555
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 556
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 557
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 558
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 559
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 561
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 562
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 563
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 564
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 565
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 567
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 568
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 569
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 570
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 571
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 572
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 574
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 576
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 577
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 578
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 579
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 583
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 584
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 585
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 586
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 588
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 589
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 590
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 591
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 592
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 593
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 595
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 596
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 598
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 599
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 601
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 603
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 605
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 606
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 608
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 609
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 611
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 612
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 613
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 615
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 616
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 618
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 619
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 624
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 626
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 628
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 629
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 630
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 633
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 635
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 637
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 639
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 640
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 642
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 679
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 681
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 684
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 688
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 692
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 696
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 697
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 698
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 699
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 701
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 703
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 704
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 705
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 708
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 711
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 713
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 738
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 739
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 741
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 744
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 754
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 755
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 758
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 760
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 768
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 769
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 797
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 801
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 803
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 806
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 812
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 814
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 815
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 817
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 818
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 819
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 825
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 842
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 843
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 845
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 846
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 847
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 848
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 851
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 854
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 855
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 857
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 858
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 859
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 860
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 861
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 862
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 864
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 866
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 868
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 869
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 872
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 873
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 876
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 879
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 880
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 883
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 885
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 890
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 892
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 894
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 896
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 897
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 910
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 911
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 912
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 914
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 915
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 916
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 938
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 960
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1165
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1167
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1175
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1181
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1182
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1190
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1195
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1206
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1216
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1226
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1235
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1244
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1246
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1251
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1303
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1314
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1318
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1330
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1332
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1333
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1342
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1391
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1397
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1408
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1410
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1411
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1419
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1427
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1435
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1444
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1447
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1457
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1465
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1512
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1535
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1545
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1546
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1557
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1590
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1679
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1681
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1684
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1688
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1692
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1696
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1697
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1698
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1699
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1701
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1703
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1704
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1705
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1708
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1711
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1713
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1738
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1739
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1741
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1744
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1754
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1755
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1758
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1760
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1768
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1769
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1797
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1806
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1816
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1889
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2219
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2235
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2236
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2238
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2242
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2243
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2244
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2246
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2251
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2259
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2292
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2302
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2303
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2307
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2312
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2314
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2315
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2317
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2318
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2319
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2320
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2321
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2322
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2323
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2324
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2326
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2327
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2328
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2329
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2330
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2331
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2332
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2333
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2334
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2335
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2337
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2340
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2342
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2345
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2347
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2361
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2387
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2390
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2391
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2397
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2399
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2400
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2401
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2402
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2403
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2405
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2406
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2407
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2408
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2409
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2410
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2411
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2412
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2414
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2416
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2417
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2419
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2420
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2422
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2425
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2427
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2429
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2431
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2434
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2435
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2436
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2437
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2438
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2439
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2441
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2442
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2443
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2444
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2445
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2447
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2448
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2449
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2450
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2452
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2455
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2456
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2457
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2458
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2461
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2462
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2465
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2473
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2474
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2476
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2487
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2490
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2491
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2492
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2495
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2496
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2499
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2501
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2502
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2503
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2504
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2506
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2507
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2508
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2509
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2510
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2511
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2512
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2513
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2514
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2515
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2516
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2518
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2519
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2520
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2522
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2524
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2525
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2526
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2528
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2529
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2530
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2531
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2532
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2533
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2534
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2535
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2536
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2537
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2538
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2539
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2540
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2541
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2542
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2543
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2544
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2545
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2546
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2547
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2550
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2551
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2552
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2553
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2554
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2555
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2556
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2557
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2558
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2559
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2561
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2562
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2563
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2564
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2565
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2567
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2568
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2569
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2570
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2571
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2572
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2574
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2576
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2577
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2578
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2579
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2583
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2584
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2585
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2586
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2588
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2589
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2590
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2591
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2592
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2593
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2595
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2596
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2598
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2599
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2601
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2603
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2605
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2606
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2608
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2609
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2611
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2612
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2613
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2615
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2616
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2618
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2619
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2624
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2626
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2628
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2629
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2630
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2633
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2635
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2637
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2639
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2640
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2642
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2679
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2681
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2684
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2688
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2692
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2696
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2697
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2698
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2699
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2701
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2703
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2704
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2705
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2708
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2711
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2713
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2738
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2739
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2741
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2744
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2754
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2755
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2758
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2760
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2768
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2769
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2797
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2801
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2803
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2806
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2809
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2812
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2814
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2815
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2816
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2817
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2818
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2819
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2825
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2830
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2837
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2839
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2840
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2841
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2842
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2843
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2844
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2845
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2846
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2847
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2848
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2850
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2851
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2852
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2853
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2854
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2855
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2856
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2857
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2858
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2859
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2860
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2861
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2862
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2863
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2864
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2865
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2866
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2868
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2869
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2871
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2872
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2873
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2874
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2875
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2876
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2877
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2878
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2879
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2880
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2883
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2885
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2887
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2888
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2889
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2890
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2891
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2892
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2894
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2895
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2896
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2897
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2904
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2906
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2909
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2910
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2911
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2912
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2914
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2915
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2916
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2929
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2938
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2940
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2943
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2954
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2956
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2957
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2958
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2959
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2960
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2963
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2964
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2965
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2966
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2967
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2968
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2969
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2972
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2974
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2975
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2977
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2978
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2983
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2984
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2985
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2986
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2988
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2990
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2991
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2992
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2993
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2994
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2997
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3000
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3001
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3002
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3009
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3012
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3013
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3014
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3015
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3016
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3024
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3025
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3028
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3029
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3030
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3031
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3033
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3034
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3037
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3042
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3043
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3044
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3046
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3048
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3050
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3051
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3052
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3053
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3055
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3056
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3057
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3058
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3059
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3060
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3061
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3062
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3065
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3071
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3072
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3074
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3075
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3077
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3080
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3081
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3082
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3083
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3084
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3085
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3087
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3088
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3089
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3091
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3092
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3093
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3094
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3095
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3096
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3097
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3098
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3099
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3100
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3101
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3102
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3104
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3122
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3124
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3131
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3132
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3152
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3165
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3166
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3167
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3175
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3176
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3181
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3182
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3189
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3190
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3195
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3206
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3215
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3216
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3219
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3221
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3223
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3226
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3235
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3236
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3238
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3242
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3243
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3244
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3246
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3251
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3259
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3292
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3302
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3303
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3307
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3312
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3314
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3315
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3317
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3318
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3319
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3320
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3321
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3322
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3323
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3324
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3326
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3327
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3328
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3329
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3330
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3331
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3332
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3333
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3334
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3335
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3337
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3340
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3342
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3345
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3347
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3361
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3387
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3390
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3391
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3397
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3399
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3400
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3401
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3402
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3403
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3405
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3406
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3407
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3408
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3409
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3410
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3411
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3412
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3414
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3416
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3417
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3419
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3420
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3422
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3425
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3427
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3429
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3431
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3434
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3435
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3436
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3437
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3438
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3439
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3441
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3442
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3443
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3444
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3445
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3447
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3448
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3449
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3450
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3452
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3455
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3456
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3457
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3458
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3461
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3462
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3465
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3473
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3474
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3476
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3487
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3490
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3491
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3492
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3495
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3496
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3499
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3501
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3502
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3503
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3504
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3506
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3507
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3508
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3509
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3510
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3511
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3512
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3513
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3514
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3515
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3516
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3518
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3519
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3520
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3522
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3524
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3525
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3526
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3528
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3529
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3530
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3531
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3532
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3533
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3534
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3535
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3536
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3537
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3538
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3539
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3540
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3541
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3542
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3543
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3544
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3545
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3546
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3547
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3550
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3551
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3552
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3553
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3554
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3555
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3556
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3557
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3558
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3559
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3561
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3562
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3563
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3564
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3565
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3567
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3568
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3569
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3570
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3571
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3572
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3574
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3576
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3577
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3578
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3579
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3583
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3584
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3585
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3586
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3588
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3589
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3590
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3591
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3592
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3593
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3595
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3596
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3598
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3599
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3601
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3603
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3605
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3606
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3608
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3609
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3611
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3612
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3613
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3615
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3616
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3618
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3619
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3624
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3626
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3628
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3629
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3630
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3633
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3635
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3637
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3639
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3640
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3642
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3679
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3681
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3684
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3688
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3692
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3696
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3697
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3698
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3699
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3701
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3703
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3704
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3705
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3708
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3711
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3713
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3738
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3739
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3741
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3744
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3754
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3755
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3758
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3760
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3768
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3769
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3797
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3801
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3803
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3806
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3809
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3812
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3814
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3815
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3816
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3817
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3818
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3819
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3825
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3830
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3837
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3839
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3840
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3841
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3842
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3843
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3844
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3845
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3846
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3847
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3848
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3850
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3851
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3852
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3853
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3854
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3855
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3856
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3857
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3858
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3859
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3860
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3861
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3862
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3863
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3864
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3865
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3866
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3868
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3869
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3871
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3872
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3873
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3874
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3875
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3876
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3877
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3878
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3879
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3880
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3883
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3885
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3887
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3888
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3889
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3890
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3891
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3892
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3894
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3895
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3896
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3897
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3904
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3906
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3909
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3910
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3911
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3912
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3914
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3915
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3916
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3929
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3938
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3940
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3943
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3954
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3956
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3957
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3958
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3959
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3960
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3963
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3964
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3965
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3966
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3967
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3968
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3969
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3972
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3974
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3975
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3977
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3978
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3983
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3984
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3985
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3986
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3988
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3990
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3991
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3992
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3993
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3994
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3997
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4000
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4001
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4002
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4009
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4012
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4013
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4014
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4015
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4016
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4017
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4024
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4025
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4028
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4029
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4030
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4031
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4033
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4034
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4037
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4042
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4043
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4044
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4046
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4048
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4050
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4051
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4052
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4053
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4055
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4056
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4057
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4058
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4059
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4060
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4061
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4062
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4065
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4071
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4072
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4074
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4075
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4077
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4080
# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.40.5_n4_db_nb_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in confusionMatrix.default(data = pred0,
## as.factor(Dry_Bean_DatasetTest$Class)): Levels are not in the same order for
## reference and data. Refactoring data to match.
db_tda_pc_5.40.5_n4_db_nb_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      333      0   76       39     1   586   44
##   BOMBAY          1    156    0        0     0     0    0
##   CALI           25      0  354        0     3     0    0
##   DERMASON        0      0    0        0     0     0    0
##   HOROZ          37      0   59     1024   574    22  746
##   SEKER           0      0    0        0     0     0    0
##   SIRA            0      0    0        0     0     0    0
## 
## Overall Statistics
##                                           
##                Accuracy : 0.3473          
##                  95% CI : (0.3327, 0.3621)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.255           
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.84091       1.00000     0.72393          0.0000
## Specificity                  0.79750       0.99975     0.99220          1.0000
## Pos Pred Value               0.30862       0.99363     0.92670             NaN
## Neg Pred Value               0.97901       1.00000     0.96349          0.7395
## Prevalence                   0.09706       0.03824     0.11985          0.2605
## Detection Rate               0.08162       0.03824     0.08676          0.0000
## Detection Prevalence         0.26446       0.03848     0.09363          0.0000
## Balanced Accuracy            0.81921       0.99987     0.85806          0.5000
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9931        0.000      0.0000
## Specificity                0.4609        1.000      1.0000
## Pos Pred Value             0.2331          NaN         NaN
## Neg Pred Value             0.9975        0.851      0.8064
## Prevalence                 0.1417        0.149      0.1936
## Detection Rate             0.1407        0.000      0.0000
## Detection Prevalence       0.6034        0.000      0.0000
## Balanced Accuracy          0.7270        0.500      0.5000
db_tda_pc_5.40.5_n4_db_nb_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      333      0   76       39     1   586   44
##   BOMBAY          1    156    0        0     0     0    0
##   CALI           25      0  354        0     3     0    0
##   DERMASON        0      0    0        0     0     0    0
##   HOROZ          37      0   59     1024   574    22  746
##   SEKER           0      0    0        0     0     0    0
##   SIRA            0      0    0        0     0     0    0
## 
## Overall Statistics
##                                           
##                Accuracy : 0.3473          
##                  95% CI : (0.3327, 0.3621)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.255           
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.84091       1.00000     0.72393          0.0000
## Specificity                  0.79750       0.99975     0.99220          1.0000
## Pos Pred Value               0.30862       0.99363     0.92670             NaN
## Neg Pred Value               0.97901       1.00000     0.96349          0.7395
## Prevalence                   0.09706       0.03824     0.11985          0.2605
## Detection Rate               0.08162       0.03824     0.08676          0.0000
## Detection Prevalence         0.26446       0.03848     0.09363          0.0000
## Balanced Accuracy            0.81921       0.99987     0.85806          0.5000
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9931        0.000      0.0000
## Specificity                0.4609        1.000      1.0000
## Pos Pred Value             0.2331          NaN         NaN
## Neg Pred Value             0.9975        0.851      0.8064
## Prevalence                 0.1417        0.149      0.1936
## Detection Rate             0.1407        0.000      0.0000
## Detection Prevalence       0.6034        0.000      0.0000
## Balanced Accuracy          0.7270        0.500      0.5000
db_tda_pc_5.40.5_n4_db_nb_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   3.473039e-01   2.550431e-01   3.326858e-01   3.621410e-01   2.605392e-01 
## AccuracyPValue  McnemarPValue 
##   1.093830e-34            NaN
db_tda_pc_5.40.5_n4_db_nb_cf0_ov_acc<-db_tda_pc_5.40.5_n4_db_nb_cf0$overall[1]
db_tda_pc_5.40.5_n4_db_nb_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.8409091   0.7975027      0.3086191      0.9790070 0.3086191
## Class: BOMBAY     1.0000000   0.9997452      0.9936306      1.0000000 0.9936306
## Class: CALI       0.7239264   0.9922027      0.9267016      0.9634938 0.9267016
## Class: DERMASON   0.0000000   1.0000000            NaN      0.7394608        NA
## Class: HOROZ      0.9930796   0.4608795      0.2331438      0.9975278 0.2331438
## Class: SEKER      0.0000000   1.0000000            NaN      0.8509804        NA
## Class: SIRA       0.0000000   1.0000000            NaN      0.8063725        NA
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.8409091 0.4515254 0.09705882     0.08161765
## Class: BOMBAY   1.0000000 0.9968051 0.03823529     0.03823529
## Class: CALI     0.7239264 0.8128588 0.11985294     0.08676471
## Class: DERMASON 0.0000000        NA 0.26053922     0.00000000
## Class: HOROZ    0.9930796 0.3776316 0.14166667     0.14068627
## Class: SEKER    0.0000000        NA 0.14901961     0.00000000
## Class: SIRA     0.0000000        NA 0.19362745     0.00000000
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA           0.26446078         0.8192059
## Class: BOMBAY             0.03848039         0.9998726
## Class: CALI               0.09362745         0.8580646
## Class: DERMASON           0.00000000         0.5000000
## Class: HOROZ              0.60343137         0.7269795
## Class: SEKER              0.00000000         0.5000000
## Class: SIRA               0.00000000         0.5000000
db_tda_pc_5.40.5_n4_db_nb_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n4_db_nb_cf0$byClass[5:7]


###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_pca_5.40.5_nb_n4_3_fold<-(db_nb_fit_re - db_tda_pc_5.40.5_n4_nb_fit_re)
diff_drybean_tda_pca_5.40.5_nb_n4_3_fold
##      Accuracy
## 1 -0.05515025
## 2 -0.07877564
## 3 -0.08042655
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_nb.n4_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_nb_n4_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_nb.n4_3_fold
## $probLeft
## [1] 0.75
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_nb.n4_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_nb.n4_3_fold$probLeft/bst_dbf_db_tda_pca_5.40.5_nb.n4_3_fold$probRight
bst_dbf_db_tda_pca_5.40.5_nb.n4_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_nb.n4_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_nb_n4_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_nb.n4_3_fold
## $winLeft
## [1] 0.9913333
## 
## $winRope
## [1] 0.008666667
## 
## $winRight
## [1] 0
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_nb.n4_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_nb_n4_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_nb.n4_3_fold
## $left
## [1] 0.9886323
## 
## $rope
## [1] 0.004801299
## 
## $right
## [1] 0.006566353
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.40.5_nb_n4_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_nb.n4_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_nb_n4_3_fold))
#bf_tda_pca_5.40.5_nb.n4_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.40.5_nb_n4_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_pca_5.40.5_nb_n4_3_fold)
## t = -8.7517, df = 2, p-value = 0.01281
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.10657855 -0.03632308
## sample estimates:
##   mean of x 
## -0.07145081
### Test set diff
diff_drybean_tda_pca_5.40.5_nb.n4_test<-(db_nb_cf_ov_acc - db_tda_pc_5.40.5_n4_db_nb_cf0_ov_acc)
diff_drybean_tda_pca_5.40.5_nb.n4_test
##  Accuracy 
## 0.5470588
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_dbf_db_tda_pca_5.40.5_nb.n4_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_nb.n4_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.40.5_nb.n4_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_dbf_db_tda_pca_5.40.5_nb.n4_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_nb.n4_test$probLeft/bst_dbf_db_tda_pca_5.40.5_nb.n4_test$probRight
bst_dbf_db_tda_pca_5.40.5_nb.n4_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_dbf_db_tda_pca_5.40.5_nb.n4_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_nb.n4_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.40.5_nb.n4_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1616
## 
## $winRight
## [1] 0.8384
# Bayesian Correlated Test

bct_dbf_db_tda_pca_5.40.5_nb.n4_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_nb.n4_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.40.5_nb.n4_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_nb.n4_test)))

#BayesFactor
#bf_tda_pca_5.40.5_nb.n4_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_nb.n4_test)) #bf_tda_pca_5.40.5_nb.n4_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_nb.n4_test))

##Node5

#DryBean_TDA_PC_5.40.5_n5_NbFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.40.5.n5.vec, 
 #               method = 'nb', 
 #              trControl = fitControl,
 #               metric='Accuracy')

#DryBean_TDA_PC_5.40.5_n5_NbFit0
#DryBean_TDA_PC_5.40.5_n5_NbFit0$resample
#db_tda_pc_5.40.5_n5_nb_fit_re<-DryBean_TDA_PC_5.40.5_n5_NbFit0$resample[1]

#summary(DryBean_TDA_PC_5.40.5_n5_NbFit0)

# Predict outcome using DryBean_TDA_PC_5.40.5_n5_NbFit0 from training data based on testing data
#pred0 <- predict(DryBean_TDA_PC_5.40.5_n5_NbFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
#db_tda_pc_5.40.5_n5_db_nb_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
#db_tda_pc_5.40.5_n5_db_nb_cf0
#db_tda_pc_5.40.5_n5_db_nb_cf0 
#db_tda_pc_5.40.5_n5_db_nb_cf0$overall
#db_tda_pc_5.40.5_n5_db_nb_cf0_ov_acc<-db_tda_pc_5.40.5_n5_db_nb_cf0$overall[1]
#db_tda_pc_5.40.5_n5_db_nb_cf0$byClass
#db_tda_pc_5.40.5_n5_db_nb_cf0_pre_rec_f1<-db_tda_pc_5.40.5_n5_db_nb_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

#diff_drybean_tda_pca_5.40.5_nb_n5_3_fold<-(db_nb_fit_re - db_tda_pc_5.40.5_n5_nb_fit_re)
#diff_drybean_tda_pca_5.40.5_nb_n5_3_fold

## Bayesian Tests 3-fold diff

# Bayesian Sign Test

#bst_dbf_db_tda_pca_5.40.5_nb.n5_3_fold<-#BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_nb_n5_3_fold),-0.01,0.01)
#bst_dbf_db_tda_pca_5.40.5_nb.n5_3_fold

# Odds Left Bayesian Sign Test 

#bst_dbf_db_tda_pca_5.40.5_nb.n5_3_fold_odds.left<-bst_dbf_db_tda_pca_5.40.5_nb.n5_3_fold$probLeft/#bst_dbf_db_tda_pca_5.40.5_nb.n5_3_fold$probRight
#bst_dbf_db_tda_pca_5.40.5_nb.n5_3_fold_odds.left

# Bayesian Signed Rank Test

#bsr_dbf_db_tda_pca_5.40.5_nb.n5_3_fold<-#BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_nb_n4_3_fold),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.40.5_nb.n5_3_fold

# Bayesian Correlated Test

#bct_dbf_db_tda_pca_5.40.5_nb.n5_3_fold<-#correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_nb_n5_3_fold),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.40.5_nb.n5_3_fold

# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_nb_n5_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_pca_5.40.5_nb.n5_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_nb_n5_3_fold))
#bf_tda_pca_5.40.5_nb.n5_3_fold

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_nb_n5_3_fold))


### Test set diff
#diff_drybean_tda_pca_5.40.5_nb.n5_test<-(db_nb_cf_ov_acc - db_tda_pc_5.40.5_n5_db_nb_cf0_ov_acc)
#diff_drybean_tda_pca_5.40.5_nb.n5_test


## Bayesian Tests Test set diff

# Bayesian Sign Test

#bst_dbf_db_tda_pca_5.40.5_nb.n5_test<-#BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.40.5_nb.n5_test),-0.01,0.01)
#bst_dbf_db_tda_pca_5.40.5_nb.n5_test

# Odds Left Bayesian Sign Test 

#bst_dbf_db_tda_pca_5.40.5_nb.n5_test_odds.left<-bst_dbf_db_tda_pca_5.40.5_nb.n5_test$probLeft/#bst_dbf_db_tda_pca_5.40.5_nb.n5_test$probRight
#bst_dbf_db_tda_pca_5.40.5_nb.n5_test_odds.left

# Bayesian Signed Rank Test

#bsr_dbf_db_tda_pca_5.40.5_nb.n5_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.40.5_nb.n5_test),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.40.5_nb.n5_test

# Bayesian Correlated Test

#bct_dbf_db_tda_pca_5.40.5_nb.n5_test<-#correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.40.5_nb.n5_test),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.40.5_nb.n5_test

# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.40.5_nb.n5_test)))

#BayesFactor
#bf_tda_pca_5.40.5_nb.n5_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.40.5_nb.n5_test)) #bf_tda_pca_5.40.5_nb.n5_test

#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.40.5_nb.n5_test))

##With TDA KDE filter 5 intervals, 50% overlap, 5 bins 
##Node1

DryBean_TDA_KDE_5.40.5_n1_NbFit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.40.5.n1.vec, 
                method = 'nb', 
                trControl = fitControl,
                metric='Accuracy')
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 78
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 92
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 122
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 420
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 563
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 588
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 591
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 611
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 619
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 635
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 806
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 819
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 830
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 854
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1401
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1507
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1956
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2491
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2499
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 122
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 420
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 512
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 529
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 563
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 588
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 591
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 611
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 619
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 635
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 819
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 825
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 830
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 844
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 854
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 959
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1391
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1400
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1401
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1420
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1438
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1474
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1507
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1574
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1754
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1873
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1956
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2071
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2320
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2491
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2499
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 87
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 94
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 331
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 452
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 516
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 518
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 519
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 816
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 818
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 830
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 978
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1087
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1323
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1334
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1390
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1391
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1397
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1496
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1696
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1812
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1909
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1910
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1915
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1940
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2124
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2131
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2340
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2492
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2495
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2496
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 94
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 236
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 331
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 452
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 516
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 518
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 519
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 545
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 577
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 739
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 816
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 818
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 825
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 830
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 978
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1087
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1323
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1387
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1390
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1397
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1496
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1535
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1554
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1696
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1812
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1852
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1909
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1910
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1915
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1940
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2124
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2131
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2340
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2492
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2495
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2496
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 80
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 87
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 98
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 319
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 399
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 442
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 583
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 584
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 639
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 814
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 815
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 830
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 885
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1332
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1361
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1387
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1457
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1522
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1574
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1612
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1624
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1866
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1954
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2416
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2499
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2501
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2502
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 80
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 87
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 98
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 319
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 397
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 399
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 442
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 501
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 529
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 578
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 583
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 584
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 639
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 814
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 815
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 830
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 885
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1059
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1332
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1361
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1387
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1407
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1457
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1487
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1522
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1541
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1574
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1612
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1837
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1857
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1866
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1954
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1988
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2317
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2416
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2499
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2501
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2502
DryBean_TDA_KDE_5.40.5_n1_NbFit0
## Naive Bayes 
## 
## 7503 samples
##   16 predictor
##    7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 5001, 5004, 5001 
## Resampling results across tuning parameters:
## 
##   usekernel  Accuracy   Kappa    
##   FALSE      0.9161661  0.8992656
##    TRUE      0.9148348  0.8976668
## 
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
##  parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = FALSE and adjust
##  = 1.
DryBean_TDA_KDE_5.40.5_n1_NbFit0$resample
##    Accuracy     Kappa Resample
## 1 0.9228617 0.9072658    Fold1
## 2 0.9143657 0.8970742    Fold2
## 3 0.9112710 0.8934567    Fold3
nb_tda_kde_5.40.5_n1_nb_fit_re<-DryBean_TDA_KDE_5.40.5_n1_NbFit0$resample[1]

summary(DryBean_TDA_KDE_5.40.5_n1_NbFit0)
##             Length Class      Mode     
## apriori      7     table      numeric  
## tables      16     -none-     list     
## levels       7     -none-     character
## call         5     -none-     call     
## x           16     data.frame list     
## usekernel    1     -none-     logical  
## varnames    16     -none-     character
## xNames      16     -none-     character
## problemType  1     -none-     character
## tuneValue    3     data.frame list     
## obsLevels    7     -none-     character
## param        0     -none-     list
#Predict outcome using DryBean_TDA_KDE_5.40.5_n1_NbFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.40.5_n1_NbFit0, newdata= Dry_Bean_DatasetTest)
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 89
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 448
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 504
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 514
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 541
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 568
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 578
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 608
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 642
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 856
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 878
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 943
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 977
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 992
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 993
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 994
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1000
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1001
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1002
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1009
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1013
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1015
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1017
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1033
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1571
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1615
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1629
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2206
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2223
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2235
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2236
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2238
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2242
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2243
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2244
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2246
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2251
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2292
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2302
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2303
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2307
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2312
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2314
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2315
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2318
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2320
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2321
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2322
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2323
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2324
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2326
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2328
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2330
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2331
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2332
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2333
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2335
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2337
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2342
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2345
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2387
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2401
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2409
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2429
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2447
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2461
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2502
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2554
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2564
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2567
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3085
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3615
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3630
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3637
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3688
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3692
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3701
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3705
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3708
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3713
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3738
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3741
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3744
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3754
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3755
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3758
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3760
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3768
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3769
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3797
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3801
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3806
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3809
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3812
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3814
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3815
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3816
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3817
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3818
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3819
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3825
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3837
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3839
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3840
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3841
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3842
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3843
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3844
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3845
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3846
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3847
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3848
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3850
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3851
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3852
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3853
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3854
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3855
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3856
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3858
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3860
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3861
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3862
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3863
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3864
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3865
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3866
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3868
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3869
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3871
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3872
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3873
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3875
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3876
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3877
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3878
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3880
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3883
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3885
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3887
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3888
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3889
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3890
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3892
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3896
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3897
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3909
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3910
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3911
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3912
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3915
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3916
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3929
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3938
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3940
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3943
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3957
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3958
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3959
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3960
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3963
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3964
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3965
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3967
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3968
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3969
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3972
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3974
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3975
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3977
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3978
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3983
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3984
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3985
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3986
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3988
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3990
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3991
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3992
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3993
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3997
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4000
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4001
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4002
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4009
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4012
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4013
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4014
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4016
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4017
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4025
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4028
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4029
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4030
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4052
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4055
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4074
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4079
# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.40.5_n1_db_nb_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
nb_tda_kde_5.40.5_n1_db_nb_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      282      0   30        0     1     3    0
##   BOMBAY          1    156    1        0     0     0    0
##   CALI           66      0  438        0    10     0    0
##   DERMASON        0      0    0      768     3     1   14
##   HOROZ           4      0    9        1   552     1    5
##   SEKER           7      0    1      204     0   588   93
##   SIRA           36      0   10       90    12    15  678
## 
## Overall Statistics
##                                           
##                Accuracy : 0.8485          
##                  95% CI : (0.8372, 0.8594)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.8182          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.71212       1.00000      0.8957          0.7225
## Specificity                  0.99077       0.99949      0.9788          0.9940
## Pos Pred Value               0.89241       0.98734      0.8521          0.9771
## Neg Pred Value               0.96971       1.00000      0.9857          0.9104
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.06912       0.03824      0.1074          0.1882
## Detection Prevalence         0.07745       0.03873      0.1260          0.1926
## Balanced Accuracy            0.85145       0.99975      0.9373          0.8583
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9550       0.9671      0.8582
## Specificity                0.9943       0.9122      0.9505
## Pos Pred Value             0.9650       0.6585      0.8062
## Neg Pred Value             0.9926       0.9937      0.9654
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1353       0.1441      0.1662
## Detection Prevalence       0.1402       0.2189      0.2061
## Balanced Accuracy          0.9747       0.9396      0.9043
nb_tda_kde_5.40.5_n1_db_nb_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      282      0   30        0     1     3    0
##   BOMBAY          1    156    1        0     0     0    0
##   CALI           66      0  438        0    10     0    0
##   DERMASON        0      0    0      768     3     1   14
##   HOROZ           4      0    9        1   552     1    5
##   SEKER           7      0    1      204     0   588   93
##   SIRA           36      0   10       90    12    15  678
## 
## Overall Statistics
##                                           
##                Accuracy : 0.8485          
##                  95% CI : (0.8372, 0.8594)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.8182          
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.71212       1.00000      0.8957          0.7225
## Specificity                  0.99077       0.99949      0.9788          0.9940
## Pos Pred Value               0.89241       0.98734      0.8521          0.9771
## Neg Pred Value               0.96971       1.00000      0.9857          0.9104
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.06912       0.03824      0.1074          0.1882
## Detection Prevalence         0.07745       0.03873      0.1260          0.1926
## Balanced Accuracy            0.85145       0.99975      0.9373          0.8583
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9550       0.9671      0.8582
## Specificity                0.9943       0.9122      0.9505
## Pos Pred Value             0.9650       0.6585      0.8062
## Neg Pred Value             0.9926       0.9937      0.9654
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1353       0.1441      0.1662
## Detection Prevalence       0.1402       0.2189      0.2061
## Balanced Accuracy          0.9747       0.9396      0.9043
nb_tda_kde_5.40.5_n1_db_nb_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.8485294      0.8182327      0.8371572      0.8594001      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
nb_tda_kde_5.40.5_n1_db_nb_cf0_ov_acc<-nb_tda_kde_5.40.5_n1_db_nb_cf0$overall[1]
nb_tda_kde_5.40.5_n1_db_nb_cf0$byClas1
## NULL
nb_tda_kde_5.40.5_n1_db_nb_cf0_pre_rec_f1<-nb_tda_kde_5.40.5_n1_db_nb_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_kde_5.40.5_nb_n1_3_fold<-(db_nb_fit_re - nb_tda_kde_5.40.5_n1_nb_fit_re)
diff_drybean_tda_kde_5.40.5_nb_n1_3_fold
##       Accuracy
## 1 -0.014801257
## 2 -0.016631322
## 3 -0.008532551
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_nb.n1_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_nb_n1_3_fold),-0.01,0.01)
bst_tda_kde_5.40.5_nb.n1_3_fold
## $probLeft
## [1] 0.5
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_nb.n1_3_fold_odds.left<-bst_tda_kde_5.40.5_nb.n1_3_fold$probLeft/bst_tda_kde_5.40.5_nb.n1_3_fold$probRight
bst_tda_kde_5.40.5_nb.n1_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_nb.n1_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_nb_n1_3_fold),-0.01,0.01)
bsr_tda_kde_5.40.5_nb.n1_3_fold
## $winLeft
## [1] 0.6965
## 
## $winRope
## [1] 0.3035
## 
## $winRight
## [1] 0
# Bayesian Correlated Test

bct_tda_kde_5.40.5_nb.n1_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_nb_n1_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_nb.n1_3_fold
## $left
## [1] 0.8192258
## 
## $rope
## [1] 0.173563
## 
## $right
## [1] 0.007211266
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.40.5_nb_n1_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_kde_5.40.5_nb.n1_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_nb_n1_3_fold))
#bf_tda_kde_5.40.5_nb.n1_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.40.5_nb_n1_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_kde_5.40.5_nb_n1_3_fold)
## t = -5.4326, df = 2, p-value = 0.03225
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.023872519 -0.002770901
## sample estimates:
##   mean of x 
## -0.01332171
### Test set diff
diff_drybean_tda_kde_5.40.5_nb.n1_test<-(db_svm_cf_ov_acc - nb_tda_kde_5.40.5_n1_db_nb_cf0_ov_acc)
diff_drybean_tda_kde_5.40.5_nb.n1_test
##   Accuracy 
## 0.07892157
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_nb.n1_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_nb.n1_test),-0.01,0.01)
bst_tda_kde_5.40.5_nb.n1_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_nb.n1_test_odds.left<-bst_tda_kde_5.40.5_nb.n1_test$probLeft/bst_tda_kde_5.40.5_nb.n1_test$probRight
bst_tda_kde_5.40.5_nb.n1_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_nb.n1_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_nb.n1_test),-0.01,0.01)
bsr_tda_kde_5.40.5_nb.n1_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1610667
## 
## $winRight
## [1] 0.8389333
# Bayesian Correlated Test

bct_tda_kde_5.40.5_nb.n1_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_nb.n1_test),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_nb.n1_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_nb.n1_test)))

#BayesFactor
#bf_tda_kde_5.40.5_nb.n1_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_nb.n1_test)) #bf_tda_pca_5.40.5_nb.n1_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_nb.n1_test))

##Node2

DryBean_TDA_KDE_5.40.5_n2_NbFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.40.5.n2.vec, 
                method = 'nb', 
                trControl = fitControl,
               metric='Accuracy')
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 476
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1065
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1865
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2216
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 476
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 490
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1065
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1865
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1872
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2216
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2221
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2577
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 132
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 176
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 405
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 448
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 473
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1097
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1845
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1914
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1929
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2015
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2082
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2531
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 132
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 176
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 405
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 422
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 448
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 496
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1097
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1845
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1853
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1929
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1988
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2015
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2082
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2088
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2531
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2542
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2583
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 85
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 681
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 895
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1029
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1037
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1859
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1940
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1966
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 81
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 85
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 176
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 681
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 895
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1859
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1966
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2581
DryBean_TDA_KDE_5.40.5_n2_NbFit0
## Naive Bayes 
## 
## 8024 samples
##   16 predictor
##    6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 5349, 5349, 5350 
## Resampling results across tuning parameters:
## 
##   usekernel  Accuracy   Kappa    
##   FALSE      0.8535645  0.8111121
##    TRUE      0.8585504  0.8171406
## 
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
##  parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = TRUE and adjust
##  = 1.
DryBean_TDA_KDE_5.40.5_n2_NbFit0$resample
##    Accuracy     Kappa Resample
## 1 0.8628037 0.8226321    Fold1
## 2 0.8456075 0.8003390    Fold2
## 3 0.8672401 0.8284508    Fold3
nb_tda_kde_5.40.5_n2_nb_fit_re<-DryBean_TDA_KDE_5.40.5_n2_NbFit0$resample[1]

summary(DryBean_TDA_KDE_5.40.5_n2_NbFit0)
##             Length Class      Mode     
## apriori      6     table      numeric  
## tables      16     -none-     list     
## levels       6     -none-     character
## call         6     -none-     call     
## x           16     data.frame list     
## usekernel    1     -none-     logical  
## varnames    16     -none-     character
## xNames      16     -none-     character
## problemType  1     -none-     character
## tuneValue    3     data.frame list     
## obsLevels    6     -none-     character
## param        0     -none-     list
# Predict outcome using DryBean_TDA_KDE_5.40.5_n2_NbFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.40.5_n2_NbFit0, newdata= Dry_Bean_DatasetTest)
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 78
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 79
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 80
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 81
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 82
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 83
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 84
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 85
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 86
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 87
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 88
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 89
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 90
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 91
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 92
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 93
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 94
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 95
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 96
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 97
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 98
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 99
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 100
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 101
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 102
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 104
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 122
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 124
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 131
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 132
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 166
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 176
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 182
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 189
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 190
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 215
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 221
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 226
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 242
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 246
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 302
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 318
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 319
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 320
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 324
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 327
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 347
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 401
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 416
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 422
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 427
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 429
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 436
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 441
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 501
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 506
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 515
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 518
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 534
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 841
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 856
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 878
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 904
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 943
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 954
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 958
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 959
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 965
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 966
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 967
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 968
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 969
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 974
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 975
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 977
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 978
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 983
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 984
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 985
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 986
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 988
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 990
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 991
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 992
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 993
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 994
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 997
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1000
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1001
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1002
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1009
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1012
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1013
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1014
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1015
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1016
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1017
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1024
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1025
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1028
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1029
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1030
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1031
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1033
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1034
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1037
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1042
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1043
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1044
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1046
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1048
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1050
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1051
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1052
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1053
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1055
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1056
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1057
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1058
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1059
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1060
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1061
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1062
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1065
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1071
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1072
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1074
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1075
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1077
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1080
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1081
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1082
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1083
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1084
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1085
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1087
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1088
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1089
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1091
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1092
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1093
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1094
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1095
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1096
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1097
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1098
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1099
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1100
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1101
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1102
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1104
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1122
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1124
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1131
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1132
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1152
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1243
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1317
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1323
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1324
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1327
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1331
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1345
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1347
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1361
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1399
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1400
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1402
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1406
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1443
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1448
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1449
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1450
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1455
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1456
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1462
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1476
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1487
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1491
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1508
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1509
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1511
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1513
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1516
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1519
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1520
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1522
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1525
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1532
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1537
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1538
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1539
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1540
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1541
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1542
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1543
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1544
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1550
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1551
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1552
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1553
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1554
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1555
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1556
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1558
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1559
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1561
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1565
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1567
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1568
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1569
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1570
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1571
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1572
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1574
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1576
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1578
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1579
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1583
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1584
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1585
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1586
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1588
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1589
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1590
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1591
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1592
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1593
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1595
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1596
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1598
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1599
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1601
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1603
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1605
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1606
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1608
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1609
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1611
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1612
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1613
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1615
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1616
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1618
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1619
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1624
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1626
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1628
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1629
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1630
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1633
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1635
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1637
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1639
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1640
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1642
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2051
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2053
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2057
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2087
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2152
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2167
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2175
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2181
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2182
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2189
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2190
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2195
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2206
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2215
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2216
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2223
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2226
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3024
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3025
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3028
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3029
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3030
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3031
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3033
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3034
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3037
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3042
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3043
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3044
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3046
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3048
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3050
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3051
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3052
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3053
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3055
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3056
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3057
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3058
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3059
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3060
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3061
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3062
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3065
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3071
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3072
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3074
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3075
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3081
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3082
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3083
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3084
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3085
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3087
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3088
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3091
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3092
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3093
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3095
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3097
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3100
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3221
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3238
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3329
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3330
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3335
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3410
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3444
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3542
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3626
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3703
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3961
# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.40.5_n2_db_nb_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in confusionMatrix.default(data = pred0,
## as.factor(Dry_Bean_DatasetTest$Class)): Levels are not in the same order for
## reference and data. Refactoring data to match.
nb_tda_kde_5.40.5_n2_db_nb_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      300      2   67        0     3     2    3
##   BOMBAY          0      0    0        0     0     0    0
##   CALI           68     80  412        0    14     0    6
##   DERMASON        0     64    0      985     5    52   89
##   HOROZ           4      3    7        1   551     0   19
##   SEKER           5      7    1       21     0   541   28
##   SIRA           19      0    2       56     5    13  645
## 
## Overall Statistics
##                                           
##                Accuracy : 0.8417          
##                  95% CI : (0.8301, 0.8527)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.807           
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.75758       0.00000      0.8425          0.9266
## Specificity                  0.97910       1.00000      0.9532          0.9304
## Pos Pred Value               0.79576           NaN      0.7103          0.8243
## Neg Pred Value               0.97408       0.96176      0.9780          0.9730
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.07353       0.00000      0.1010          0.2414
## Detection Prevalence         0.09240       0.00000      0.1422          0.2929
## Balanced Accuracy            0.86834       0.50000      0.8979          0.9285
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9533       0.8898      0.8165
## Specificity                0.9903       0.9821      0.9711
## Pos Pred Value             0.9419       0.8972      0.8716
## Neg Pred Value             0.9923       0.9807      0.9566
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1350       0.1326      0.1581
## Detection Prevalence       0.1434       0.1478      0.1814
## Balanced Accuracy          0.9718       0.9360      0.8938
nb_tda_kde_5.40.5_n2_db_nb_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      300      2   67        0     3     2    3
##   BOMBAY          0      0    0        0     0     0    0
##   CALI           68     80  412        0    14     0    6
##   DERMASON        0     64    0      985     5    52   89
##   HOROZ           4      3    7        1   551     0   19
##   SEKER           5      7    1       21     0   541   28
##   SIRA           19      0    2       56     5    13  645
## 
## Overall Statistics
##                                           
##                Accuracy : 0.8417          
##                  95% CI : (0.8301, 0.8527)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.807           
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.75758       0.00000      0.8425          0.9266
## Specificity                  0.97910       1.00000      0.9532          0.9304
## Pos Pred Value               0.79576           NaN      0.7103          0.8243
## Neg Pred Value               0.97408       0.96176      0.9780          0.9730
## Prevalence                   0.09706       0.03824      0.1199          0.2605
## Detection Rate               0.07353       0.00000      0.1010          0.2414
## Detection Prevalence         0.09240       0.00000      0.1422          0.2929
## Balanced Accuracy            0.86834       0.50000      0.8979          0.9285
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9533       0.8898      0.8165
## Specificity                0.9903       0.9821      0.9711
## Pos Pred Value             0.9419       0.8972      0.8716
## Neg Pred Value             0.9923       0.9807      0.9566
## Prevalence                 0.1417       0.1490      0.1936
## Detection Rate             0.1350       0.1326      0.1581
## Detection Prevalence       0.1434       0.1478      0.1814
## Balanced Accuracy          0.9718       0.9360      0.8938
nb_tda_kde_5.40.5_n2_db_nb_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.8416667      0.8069643      0.8300987      0.8527430      0.2605392 
## AccuracyPValue  McnemarPValue 
##      0.0000000            NaN
nb_tda_kde_5.40.5_n2_db_nb_cf0_ov_acc<-nb_tda_kde_5.40.5_n2_db_nb_cf0$overall[1]
nb_tda_kde_5.40.5_n2_db_nb_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.7575758   0.9790988      0.7957560      0.9740751 0.7957560
## Class: BOMBAY     0.0000000   1.0000000            NaN      0.9617647        NA
## Class: CALI       0.8425358   0.9532164      0.7103448      0.9780000 0.7103448
## Class: DERMASON   0.9266228   0.9303944      0.8242678      0.9729636 0.8242678
## Class: HOROZ      0.9532872   0.9902913      0.9418803      0.9922747 0.9418803
## Class: SEKER      0.8898026   0.9821429      0.8971808      0.9807305 0.8971808
## Class: SIRA       0.8164557   0.9711246      0.8716216      0.9565868 0.8716216
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.7575758 0.7761966 0.09705882     0.07352941
## Class: BOMBAY   0.0000000        NA 0.03823529     0.00000000
## Class: CALI     0.8425358 0.7708138 0.11985294     0.10098039
## Class: DERMASON 0.9266228 0.8724535 0.26053922     0.24142157
## Class: HOROZ    0.9532872 0.9475494 0.14166667     0.13504902
## Class: SEKER    0.8898026 0.8934765 0.14901961     0.13259804
## Class: SIRA     0.8164557 0.8431373 0.19362745     0.15808824
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA           0.09240196         0.8683373
## Class: BOMBAY             0.00000000         0.5000000
## Class: CALI               0.14215686         0.8978761
## Class: DERMASON           0.29289216         0.9285086
## Class: HOROZ              0.14338235         0.9717892
## Class: SEKER              0.14779412         0.9359727
## Class: SIRA               0.18137255         0.8937902
nb_tda_kde_5.40.5_n2_db_nb_cf0_pre_rec_f1<-nb_tda_kde_5.40.5_n2_db_nb_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_kde_5.40.5_nb_n2_3_fold<-(db_nb_fit_re - nb_tda_kde_5.40.5_n2_nb_fit_re)
diff_drybean_tda_kde_5.40.5_nb_n2_3_fold
##     Accuracy
## 1 0.04525672
## 2 0.05212695
## 3 0.03549834
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_nb.n2_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_nb_n2_3_fold),-0.01,0.01)
bst_tda_kde_5.40.5_nb.n2_3_fold
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_nb.n2_3_fold_odds.left<-bst_tda_kde_5.40.5_nb.n2_3_fold$probLeft/bst_tda_kde_5.40.5_nb.n2_3_fold$probRight
bst_tda_kde_5.40.5_nb.n2_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_nb.n2_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_nb_n2_3_fold),-0.01,0.01)
bsr_tda_kde_5.40.5_nb.n2_3_fold
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.008766667
## 
## $winRight
## [1] 0.9912333
# Bayesian Correlated Test

bct_tda_kde_5.40.5_nb.n2_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_nb_n2_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_nb.n2_3_fold
## $left
## [1] 0.005181889
## 
## $rope
## [1] 0.007511021
## 
## $right
## [1] 0.9873071
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.40.5_nb_n2_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_kde_5.40.5_nb.n2_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_nb_n2_3_fold))
#bf_tda_kde_5.40.5_nb.n2_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.40.5_nb_n2_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_kde_5.40.5_nb_n2_3_fold)
## t = 9.1814, df = 2, p-value = 0.01166
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  0.02353655 0.06505146
## sample estimates:
## mean of x 
##  0.044294
### Test set diff
diff_drybean_tda_kde_5.40.5_nb.n2_test<-(db_svm_cf_ov_acc - nb_tda_kde_5.40.5_n2_db_nb_cf0_ov_acc)
diff_drybean_tda_kde_5.40.5_nb.n2_test
##   Accuracy 
## 0.08578431
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_nb.n2_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_nb.n2_test),-0.01,0.01)
bst_tda_kde_5.40.5_nb.n2_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_nb.n2_test_odds.left<-bst_tda_kde_5.40.5_nb.n2_test$probLeft/bst_tda_kde_5.40.5_nb.n2_test$probRight
bst_tda_kde_5.40.5_nb.n2_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_nb.n2_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_nb.n2_test),-0.01,0.01)
bsr_tda_kde_5.40.5_nb.n2_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1609
## 
## $winRight
## [1] 0.8391
# Bayesian Correlated Test

bct_tda_kde_5.40.5_nb.n2_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_nb.n2_test),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_nb.n2_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_nb.n2_test)))

#BayesFactor
#bf_tda_kde_5.40.5_nb.n2_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_nb.n2_test)) #bf_tda_kde_5.40.5_nb.n2_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_nb.n2_test))

##Node3

#DryBean_TDA_KDE_5.40.5_n3_NbFit0 <- train(as.factor(Class) ~ ., data = #tda.m_dry_bean_dataset_5.40.5.n3.vec, 
#                method = 'nb', 
#                trControl = fitControl,
#                metric='Accuracy')

#DryBean_TDA_KDE_5.40.5_n3_NbFit0
#DryBean_TDA_KDE_5.40.5_n3_NbFit0$resample
#nb_tda_kde_5.40.5_n3_nb_fit_re<-DryBean_TDA_KDE_5.40.5_n3_NbFit0$resample[1]

#summary(DryBean_TDA_KDE_5.40.5_n3_NbFit0)

 #Predict outcome using DryBean_TDA_KDE_5.40.5_n3_NbFit0 from training data based on testing data
#pred0 <- predict(DryBean_TDA_KDE_5.40.5_n3_NbFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
#nb_tda_kde_5.40.5_n3_db_nb_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
#nb_tda_kde_5.40.5_n3_db_nb_cf0
#nb_tda_kde_5.40.5_n3_db_nb_cf0 
#nb_tda_kde_5.40.5_n3_db_nb_cf0$overall
#nb_tda_kde_5.40.5_n3_db_nb_cf0_ov_acc<-nb_tda_kde_5.40.5_n3_db_nb_cf0$overall[1]
#nb_tda_kde_5.40.5_n3_db_nb_cf0$byClass
#nb_tda_kde_5.40.5_n3_db_nb_cf0_pre_rec_f1<-nb_tda_kde_5.40.5_n3_db_nb_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

#diff_drybean_tda_kde_5.40.5_nb_n3_3_fold<-(db_nb_fit_re - nb_tda_kde_5.40.5_n3_nb_fit_re)
#diff_drybean_tda_kde_5.40.5_nb_n3_3_fold

## Bayesian Tests 3-fold diff

# Bayesian Sign Test

#bst_tda_kde_5.40.5_nb.n3_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_nb_n3_3_fold),-0.01,0.01)
#bst_tda_kde_5.40.5_nb.n3_3_fold

# Odds Left Bayesian Sign Test 

#bst_tda_kde_5.40.5_nb.n3_3_fold_odds.left<-bst_tda_kde_5.40.5_nb.n3_3_fold$probLeft/bst_tda_kde_5.40.5_nb.n3_3_fold$probRight
#bst_tda_kde_5.40.5_nb.n3_3_fold_odds.left

# Bayesian Signed Rank Test

#bsr_tda_kde_5.40.5_nb.n3_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_nb_n3_3_fold),-0.01,0.01)
#bsr_tda_kde_5.40.5_nb.n3_3_fold

# Bayesian Correlated Test

#bct_tda_kde_5.40.5_nb.n3_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_nb_n3_3_fold),0.1,-0.01,0.01)
#bct_tda_kde_5.40.5_nb.n3_3_fold

# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_nb_n3_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_kde_5.40.5_nb.n3_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_nb_n3_3_fold))
#bf_tda_kde_5.40.5_nb.n3_3_fold

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_nb_n3_3_fold))


### Test set diff
#diff_drybean_tda_kde_5.40.5_nb.n3_test<-(db_svm_cf_ov_acc - nb_tda_kde_5.40.5_n3_db_nb_cf0_ov_acc)
#diff_drybean_tda_kde_5.40.5_nb.n3_test


## Bayesian Tests Test set diff

# Bayesian Sign Test

#bst_tda_kde_5.40.5_nb.n3_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_nb.n3_test),-0.01,0.01)
#bst_tda_kde_5.40.5_nb.n3_test

# Odds Left Bayesian Sign Test 

#bst_tda_kde_5.40.5_nb.n3_test_odds.left<-bst_tda_kde_5.40.5_nb.n3_test$probLeft/bst_tda_kde_5.40.5_nb.n3_test$probRight
#bst_tda_kde_5.40.5_nb.n3_test_odds.left

# Bayesian Signed Rank Test

#bsr_tda_kde_5.40.5_nb.n3_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_nb.n3_test),-0.01,0.01)
#bsr_tda_kde_5.40.5_nb.n3_test

# Bayesian Correlated Test

#bct_tda_kde_5.40.5_nb.n3_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_nb.n3_test),0.1,-0.01,0.01)
#bct_tda_kde_5.40.5_nb.n3_test

# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_nb.n3_test)))

#BayesFactor
#bf_tda_kde_5.40.5_nb.n3_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_nb.n3_test)) #bf_tda_kde_5.40.5_nb.n3_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_nb.n3_test))


##Node4

DryBean_TDA_KDE_5.40.5_n4_NbFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.40.5.n4.vec, 
                method = 'nb', 
                trControl = fitControl,
                metric='Accuracy')
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 78
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 79
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 78
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 79
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 88
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 88
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 165
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 97
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 165
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 263
DryBean_TDA_KDE_5.40.5_n4_NbFit0
## Naive Bayes 
## 
## 894 samples
##  16 predictor
##   4 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'HOROZ' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold) 
## Summary of sample sizes: 597, 594, 597 
## Resampling results across tuning parameters:
## 
##   usekernel  Accuracy   Kappa    
##   FALSE      0.9686756  0.9495951
##    TRUE      0.9675533  0.9476681
## 
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
##  parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = FALSE and adjust
##  = 1.
DryBean_TDA_KDE_5.40.5_n4_NbFit0$resample
##   Accuracy     Kappa Resample
## 1 0.956229 0.9294408    Fold1
## 2 0.970000 0.9518717    Fold2
## 3 0.979798 0.9674728    Fold3
nb_tda_kde_5.40.5_n4_nb_fit_re<-DryBean_TDA_KDE_5.40.5_n4_NbFit0$resample[1]

summary(DryBean_TDA_KDE_5.40.5_n4_NbFit0)
##             Length Class      Mode     
## apriori      4     table      numeric  
## tables      16     -none-     list     
## levels       4     -none-     character
## call         5     -none-     call     
## x           16     data.frame list     
## usekernel    1     -none-     logical  
## varnames    16     -none-     character
## xNames      16     -none-     character
## problemType  1     -none-     character
## tuneValue    3     data.frame list     
## obsLevels    4     -none-     character
## param        0     -none-     list
# Predict outcome using DryBean_TDA_KDE_5.40.5_n4_NbFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.40.5_n4_NbFit0, newdata= Dry_Bean_DatasetTest)
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 78
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 79
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 80
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 81
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 82
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 83
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 84
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 85
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 86
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 87
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 88
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 89
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 90
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 91
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 92
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 93
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 94
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 95
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 96
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 97
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 98
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 99
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 100
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 101
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 102
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 104
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 122
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 124
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 131
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 132
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 152
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 165
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 166
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 167
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 175
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 176
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 181
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 182
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 189
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 190
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 195
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 206
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 215
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 216
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 219
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 221
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 223
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 226
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 235
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 236
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 238
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 242
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 243
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 244
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 246
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 251
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 259
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 292
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 302
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 303
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 307
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 312
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 314
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 315
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 317
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 318
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 319
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 320
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 321
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 322
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 323
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 324
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 326
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 327
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 328
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 329
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 330
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 331
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 332
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 333
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 334
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 335
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 337
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 340
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 342
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 345
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 347
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 361
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 387
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 390
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 391
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 397
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 399
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 400
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 401
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 402
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 403
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 405
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 406
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 407
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 408
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 409
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 410
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 411
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 412
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 414
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 416
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 417
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 419
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 420
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 422
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 425
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 427
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 429
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 431
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 434
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 435
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 436
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 437
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 438
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 439
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 441
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 442
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 443
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 444
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 445
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 447
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 448
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 449
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 450
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 452
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 455
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 456
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 457
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 458
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 461
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 462
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 465
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 473
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 474
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 476
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 487
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 490
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 491
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 492
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 495
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 496
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 499
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 501
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 502
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 503
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 504
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 506
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 507
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 508
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 509
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 510
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 511
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 512
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 513
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 514
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 515
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 516
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 518
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 519
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 520
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 522
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 524
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 525
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 526
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 528
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 529
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 530
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 531
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 532
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 533
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 534
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 535
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 536
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 537
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 538
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 539
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 540
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 541
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 542
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 543
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 544
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 545
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 546
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 547
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 550
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 551
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 552
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 553
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 554
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 555
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 556
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 557
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 558
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 559
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 561
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 562
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 563
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 564
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 565
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 567
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 568
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 569
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 570
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 571
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 572
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 574
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 576
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 577
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 578
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 579
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 583
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 584
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 585
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 586
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 588
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 589
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 590
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 591
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 592
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 593
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 595
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 596
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 598
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 599
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 601
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 603
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 605
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 606
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 608
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 609
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 611
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 612
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 613
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 615
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 616
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 618
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 619
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 624
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 626
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 628
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 629
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 630
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 633
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 635
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 637
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 639
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 640
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 642
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 679
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 681
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 684
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 688
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 692
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 696
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 697
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 698
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 699
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 701
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 703
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 704
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 705
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 708
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 711
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 713
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 738
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 739
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 741
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 744
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 754
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 755
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 758
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 760
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 768
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 769
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 797
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 801
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 803
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 806
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 812
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 814
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 815
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 817
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 818
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 819
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 825
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 842
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 843
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 845
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 846
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 847
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 848
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 851
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 854
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 855
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 857
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 858
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 859
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 860
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 861
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 862
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 864
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 866
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 868
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 869
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 872
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 873
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 876
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 879
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 880
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 883
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 885
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 890
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 892
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 894
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 896
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 897
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 910
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 911
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 912
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 914
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 915
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 916
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 938
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 960
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1165
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1167
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1175
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1181
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1182
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1190
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1195
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1206
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1216
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1226
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1235
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1244
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1246
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1251
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1303
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1314
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1318
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1330
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1332
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1333
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1342
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1391
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1397
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1408
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1410
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1411
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1419
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1427
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1435
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1444
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1447
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1457
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1465
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1512
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1535
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1545
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1546
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1557
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1590
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1679
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1681
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1684
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1688
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1692
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1696
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1697
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1698
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1699
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1701
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1703
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1704
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1705
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1708
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1711
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1713
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1738
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1739
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1741
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1744
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1754
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1755
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1758
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1760
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1768
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1769
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1797
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1806
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1816
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1889
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2219
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2235
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2236
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2238
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2242
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2243
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2244
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2246
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2251
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2259
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2292
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2302
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2303
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2307
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2312
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2314
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2315
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2317
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2318
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2319
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2320
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2321
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2322
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2323
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2324
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2326
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2327
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2328
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2329
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2330
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2331
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2332
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2333
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2334
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2335
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2337
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2340
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2342
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2345
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2347
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2361
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2387
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2390
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2391
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2397
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2399
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2400
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2401
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2402
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2403
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2405
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2406
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2407
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2408
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2409
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2410
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2411
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2412
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2414
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2416
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2417
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2419
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2420
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2422
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2425
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2427
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2429
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2431
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2434
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2435
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2436
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2437
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2438
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2439
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2441
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2442
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2443
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2444
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2445
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2447
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2448
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2449
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2450
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2452
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2455
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2456
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2457
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2458
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2461
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2462
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2465
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2473
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2474
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2476
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2487
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2490
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2491
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2492
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2495
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2496
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2499
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2501
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2502
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2503
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2504
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2506
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2507
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2508
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2509
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2510
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2511
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2512
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2513
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2514
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2515
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2516
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2518
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2519
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2520
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2522
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2524
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2525
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2526
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2528
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2529
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2530
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2531
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2532
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2533
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2534
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2535
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2536
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2537
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2538
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2539
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2540
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2541
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2542
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2543
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2544
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2545
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2546
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2547
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2550
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2551
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2552
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2553
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2554
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2555
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2556
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2557
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2558
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2559
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2561
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2562
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2563
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2564
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2565
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2567
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2568
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2569
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2570
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2571
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2572
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2574
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2576
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2577
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2578
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2579
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2583
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2584
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2585
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2586
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2588
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2589
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2590
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2591
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2592
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2593
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2595
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2596
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2598
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2599
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2601
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2603
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2605
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2606
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2608
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2609
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2611
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2612
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2613
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2615
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2616
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2618
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2619
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2624
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2626
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2628
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2629
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2630
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2633
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2635
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2637
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2639
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2640
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2642
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2679
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2681
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2684
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2688
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2692
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2696
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2697
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2698
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2699
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2701
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2703
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2704
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2705
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2708
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2711
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2713
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2738
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2739
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2741
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2744
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2754
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2755
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2758
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2760
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2768
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2769
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2797
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2801
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2803
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2806
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2809
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2812
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2814
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2815
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2816
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2817
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2818
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2819
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2825
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2830
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2837
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2839
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2840
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2841
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2842
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2843
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2844
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2845
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2846
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2847
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2848
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2850
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2851
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2852
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2853
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2854
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2855
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2856
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2857
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2858
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2859
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2860
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2861
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2862
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2863
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2864
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2865
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2866
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2868
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2869
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2871
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2872
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2873
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2874
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2875
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2876
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2877
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2878
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2879
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2880
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2883
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2885
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2887
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2888
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2889
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2890
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2891
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2892
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2894
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2895
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2896
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2897
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2904
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2906
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2909
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2910
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2911
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2912
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2914
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2915
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2916
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2929
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2938
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2940
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2943
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2954
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2956
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2957
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2958
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2959
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2960
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2963
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2964
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2965
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2966
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2967
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2968
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2969
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2972
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2974
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2975
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2977
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2978
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2983
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2984
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2985
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2986
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2988
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2990
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2991
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2992
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2993
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2994
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2997
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3000
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3001
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3002
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3009
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3012
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3013
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3014
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3015
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3016
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3024
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3025
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3028
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3029
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3030
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3031
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3033
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3034
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3037
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3042
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3043
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3044
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3046
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3048
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3050
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3051
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3052
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3053
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3055
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3056
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3057
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3058
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3059
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3060
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3061
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3062
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3065
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3071
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3072
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3074
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3075
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3077
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3080
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3081
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3082
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3083
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3084
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3085
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3087
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3088
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3089
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3091
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3092
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3093
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3094
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3095
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3096
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3097
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3098
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3099
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3100
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3101
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3102
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3104
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3122
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3124
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3131
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3132
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3152
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3165
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3166
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3167
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3175
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3176
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3181
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3182
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3189
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3190
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3195
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3206
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3215
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3216
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3219
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3221
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3223
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3226
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3235
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3236
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3238
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3242
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3243
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3244
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3246
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3251
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3259
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3292
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3302
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3303
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3307
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3312
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3314
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3315
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3317
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3318
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3319
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3320
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3321
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3322
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3323
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3324
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3326
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3327
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3328
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3329
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3330
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3331
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3332
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3333
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3334
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3335
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3337
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3340
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3342
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3345
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3347
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3361
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3387
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3390
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3391
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3397
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3399
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3400
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3401
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3402
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3403
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3405
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3406
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3407
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3408
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3409
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3410
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3411
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3412
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3414
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3416
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3417
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3419
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3420
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3422
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3425
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3427
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3429
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3431
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3434
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3435
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3436
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3437
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3438
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3439
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3441
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3442
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3443
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3444
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3445
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3447
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3448
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3449
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3450
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3452
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3455
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3456
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3457
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3458
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3461
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3462
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3465
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3473
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3474
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3476
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3487
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3490
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3491
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3492
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3495
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3496
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3499
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3501
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3502
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3503
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3504
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3506
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3507
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3508
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3509
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3510
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3511
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3512
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3513
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3514
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3515
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3516
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3518
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3519
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3520
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3522
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3524
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3525
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3526
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3528
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3529
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3530
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3531
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3532
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3533
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3534
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3535
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3536
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3537
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3538
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3539
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3540
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3541
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3542
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3543
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3544
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3545
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3546
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3547
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3550
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3551
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3552
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3553
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3554
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3555
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3556
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3557
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3558
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3559
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3561
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3562
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3563
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3564
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3565
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3567
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3568
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3569
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3570
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3571
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3572
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3574
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3576
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3577
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3578
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3579
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3583
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3584
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3585
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3586
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3588
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3589
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3590
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3591
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3592
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3593
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3595
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3596
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3598
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3599
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3601
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3603
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3605
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3606
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3608
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3609
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3611
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3612
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3613
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3615
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3616
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3618
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3619
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3624
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3626
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3628
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3629
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3630
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3633
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3635
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3637
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3639
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3640
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3642
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3679
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3681
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3684
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3688
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3692
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3696
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3697
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3698
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3699
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3701
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3703
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3704
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3705
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3708
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3711
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3713
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3738
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3739
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3741
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3744
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3754
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3755
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3758
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3760
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3768
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3769
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3797
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3801
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3803
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3806
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3809
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3812
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3814
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3815
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3816
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3817
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3818
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3819
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3825
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3830
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3837
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3839
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3840
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3841
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3842
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3843
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3844
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3845
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3846
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3847
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3848
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3850
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3851
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3852
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3853
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3854
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3855
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3856
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3857
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3858
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3859
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3860
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3861
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3862
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3863
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3864
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3865
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3866
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3868
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3869
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3871
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3872
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3873
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3874
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3875
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3876
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3877
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3878
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3879
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3880
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3883
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3885
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3887
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3888
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3889
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3890
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3891
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3892
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3894
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3895
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3896
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3897
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3904
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3906
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3909
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3910
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3911
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3912
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3914
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3915
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3916
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3929
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3938
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3940
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3943
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3954
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3956
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3957
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3958
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3959
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3960
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3963
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3964
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3965
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3966
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3967
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3968
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3969
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3972
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3974
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3975
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3977
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3978
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3983
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3984
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3985
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3986
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3988
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3990
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3991
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3992
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3993
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3994
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3997
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4000
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4001
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4002
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4009
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4012
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4013
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4014
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4015
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4016
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4017
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4024
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4025
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4028
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4029
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4030
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4031
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4033
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4034
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4037
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4042
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4043
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4044
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4046
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4048
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4050
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4051
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4052
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4053
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4055
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4056
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4057
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4058
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4059
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4060
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4061
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4062
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4065
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4071
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4072
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4074
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4075
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4077
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4080
# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.40.5_n4_db_nb_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in confusionMatrix.default(data = pred0,
## as.factor(Dry_Bean_DatasetTest$Class)): Levels are not in the same order for
## reference and data. Refactoring data to match.
nb_tda_kde_5.40.5_n4_db_nb_cf0
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      333      0   76       39     1   586   44
##   BOMBAY          1    156    0        0     0     0    0
##   CALI           25      0  354        0     3     0    0
##   DERMASON        0      0    0        0     0     0    0
##   HOROZ          37      0   59     1024   574    22  746
##   SEKER           0      0    0        0     0     0    0
##   SIRA            0      0    0        0     0     0    0
## 
## Overall Statistics
##                                           
##                Accuracy : 0.3473          
##                  95% CI : (0.3327, 0.3621)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.255           
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.84091       1.00000     0.72393          0.0000
## Specificity                  0.79750       0.99975     0.99220          1.0000
## Pos Pred Value               0.30862       0.99363     0.92670             NaN
## Neg Pred Value               0.97901       1.00000     0.96349          0.7395
## Prevalence                   0.09706       0.03824     0.11985          0.2605
## Detection Rate               0.08162       0.03824     0.08676          0.0000
## Detection Prevalence         0.26446       0.03848     0.09363          0.0000
## Balanced Accuracy            0.81921       0.99987     0.85806          0.5000
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9931        0.000      0.0000
## Specificity                0.4609        1.000      1.0000
## Pos Pred Value             0.2331          NaN         NaN
## Neg Pred Value             0.9975        0.851      0.8064
## Prevalence                 0.1417        0.149      0.1936
## Detection Rate             0.1407        0.000      0.0000
## Detection Prevalence       0.6034        0.000      0.0000
## Balanced Accuracy          0.7270        0.500      0.5000
nb_tda_kde_5.40.5_n4_db_nb_cf0 
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
##   BARBUNYA      333      0   76       39     1   586   44
##   BOMBAY          1    156    0        0     0     0    0
##   CALI           25      0  354        0     3     0    0
##   DERMASON        0      0    0        0     0     0    0
##   HOROZ          37      0   59     1024   574    22  746
##   SEKER           0      0    0        0     0     0    0
##   SIRA            0      0    0        0     0     0    0
## 
## Overall Statistics
##                                           
##                Accuracy : 0.3473          
##                  95% CI : (0.3327, 0.3621)
##     No Information Rate : 0.2605          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.255           
##                                           
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity                  0.84091       1.00000     0.72393          0.0000
## Specificity                  0.79750       0.99975     0.99220          1.0000
## Pos Pred Value               0.30862       0.99363     0.92670             NaN
## Neg Pred Value               0.97901       1.00000     0.96349          0.7395
## Prevalence                   0.09706       0.03824     0.11985          0.2605
## Detection Rate               0.08162       0.03824     0.08676          0.0000
## Detection Prevalence         0.26446       0.03848     0.09363          0.0000
## Balanced Accuracy            0.81921       0.99987     0.85806          0.5000
##                      Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity                0.9931        0.000      0.0000
## Specificity                0.4609        1.000      1.0000
## Pos Pred Value             0.2331          NaN         NaN
## Neg Pred Value             0.9975        0.851      0.8064
## Prevalence                 0.1417        0.149      0.1936
## Detection Rate             0.1407        0.000      0.0000
## Detection Prevalence       0.6034        0.000      0.0000
## Balanced Accuracy          0.7270        0.500      0.5000
nb_tda_kde_5.40.5_n4_db_nb_cf0$overall
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   3.473039e-01   2.550431e-01   3.326858e-01   3.621410e-01   2.605392e-01 
## AccuracyPValue  McnemarPValue 
##   1.093830e-34            NaN
nb_tda_kde_5.40.5_n4_db_nb_cf0_ov_acc<-nb_tda_kde_5.40.5_n4_db_nb_cf0$overall[1]
nb_tda_kde_5.40.5_n4_db_nb_cf0$byClass
##                 Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA   0.8409091   0.7975027      0.3086191      0.9790070 0.3086191
## Class: BOMBAY     1.0000000   0.9997452      0.9936306      1.0000000 0.9936306
## Class: CALI       0.7239264   0.9922027      0.9267016      0.9634938 0.9267016
## Class: DERMASON   0.0000000   1.0000000            NaN      0.7394608        NA
## Class: HOROZ      0.9930796   0.4608795      0.2331438      0.9975278 0.2331438
## Class: SEKER      0.0000000   1.0000000            NaN      0.8509804        NA
## Class: SIRA       0.0000000   1.0000000            NaN      0.8063725        NA
##                    Recall        F1 Prevalence Detection Rate
## Class: BARBUNYA 0.8409091 0.4515254 0.09705882     0.08161765
## Class: BOMBAY   1.0000000 0.9968051 0.03823529     0.03823529
## Class: CALI     0.7239264 0.8128588 0.11985294     0.08676471
## Class: DERMASON 0.0000000        NA 0.26053922     0.00000000
## Class: HOROZ    0.9930796 0.3776316 0.14166667     0.14068627
## Class: SEKER    0.0000000        NA 0.14901961     0.00000000
## Class: SIRA     0.0000000        NA 0.19362745     0.00000000
##                 Detection Prevalence Balanced Accuracy
## Class: BARBUNYA           0.26446078         0.8192059
## Class: BOMBAY             0.03848039         0.9998726
## Class: CALI               0.09362745         0.8580646
## Class: DERMASON           0.00000000         0.5000000
## Class: HOROZ              0.60343137         0.7269795
## Class: SEKER              0.00000000         0.5000000
## Class: SIRA               0.00000000         0.5000000
nb_tda_kde_5.40.5_n4_db_nb_cf0_pre_rec_f1<-nb_tda_kde_5.40.5_n4_db_nb_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

diff_drybean_tda_kde_5.40.5_nb_n4_3_fold<-(db_nb_fit_re - nb_tda_kde_5.40.5_n4_nb_fit_re)
diff_drybean_tda_kde_5.40.5_nb_n4_3_fold
##      Accuracy
## 1 -0.04816850
## 2 -0.07226558
## 3 -0.07705955
## Bayesian Tests 3-fold diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_nb.n4_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_nb_n4_3_fold),-0.01,0.01)
bst_tda_kde_5.40.5_nb.n4_3_fold
## $probLeft
## [1] 0.75
## 
## $probRope
## [1] 0.25
## 
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_nb.n4_3_fold_odds.left<-bst_tda_kde_5.40.5_nb.n4_3_fold$probLeft/bst_tda_kde_5.40.5_nb.n4_3_fold$probRight
bst_tda_kde_5.40.5_nb.n4_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_nb.n4_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_nb_n4_3_fold),-0.01,0.01)
bsr_tda_kde_5.40.5_nb.n4_3_fold
## $winLeft
## [1] 0.9910333
## 
## $winRope
## [1] 0.008966667
## 
## $winRight
## [1] 0
# Bayesian Correlated Test

bct_tda_kde_5.40.5_nb.n4_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_nb_n4_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_nb.n4_3_fold
## $left
## [1] 0.983739
## 
## $rope
## [1] 0.007246671
## 
## $right
## [1] 0.009014324
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.40.5_nb_n4_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_kde_5.40.5_nb.n4_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_nb_n4_3_fold))
#bf_tda_kde_5.40.5_nb.n4_3_fold

#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.40.5_nb_n4_3_fold))
## 
##  One Sample t-test
## 
## data:  as.matrix(diff_drybean_tda_kde_5.40.5_nb_n4_3_fold)
## t = -7.3644, df = 2, p-value = 0.01794
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.10429316 -0.02736925
## sample estimates:
##   mean of x 
## -0.06583121
### Test set diff
diff_drybean_tda_kde_5.40.5_nb.n4_test<-(db_svm_cf_ov_acc - nb_tda_kde_5.40.5_n4_db_nb_cf0_ov_acc)
diff_drybean_tda_kde_5.40.5_nb.n4_test
##  Accuracy 
## 0.5801471
## Bayesian Tests Test set diff

# Bayesian Sign Test

bst_tda_kde_5.40.5_nb.n4_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_nb.n4_test),-0.01,0.01)
bst_tda_kde_5.40.5_nb.n4_test
## $probLeft
## [1] 0
## 
## $probRope
## [1] 0.5
## 
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test 

bst_tda_kde_5.40.5_nb.n4_test_odds.left<-bst_tda_kde_5.40.5_nb.n4_test$probLeft/bst_tda_kde_5.40.5_nb.n4_test$probRight
bst_tda_kde_5.40.5_nb.n4_test_odds.left
## [1] 0
# Bayesian Signed Rank Test

bsr_tda_kde_5.40.5_nb.n4_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_nb.n4_test),-0.01,0.01)
bsr_tda_kde_5.40.5_nb.n4_test
## $winLeft
## [1] 0
## 
## $winRope
## [1] 0.1629333
## 
## $winRight
## [1] 0.8370667
# Bayesian Correlated Test

bct_tda_kde_5.40.5_nb.n4_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_nb.n4_test),0.1,-0.01,0.01)
bct_tda_kde_5.40.5_nb.n4_test
## $left
## [1] NA
## 
## $rope
## [1] NA
## 
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_nb.n4_test)))

#BayesFactor
#bf_tda_kde_5.40.5_nb.n4_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_nb.n4_test)) #bf_tda_kde_5.40.5_nb.n4_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_nb.n4_test))

##Node5

#DryBean_TDA_KDE_5.40.5_n5_NbFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.40.5.n5.vec, 
#                method = 'nb', 
#                trControl = fitControl,
#                metric='Accuracy')

#DryBean_TDA_KDE_5.40.5_n5_NbFit0
#DryBean_TDA_KDE_5.40.5_n5_NbFit0$resample
#nb_tda_kde_5.40.5_n5_nb_fit_re<-DryBean_TDA_KDE_5.40.5_n5_NbFit0$resample[1]

#summary(DryBean_TDA_KDE_5.40.5_n5_NbFit0)

# Predict outcome using DryBean_TDA_KDE_5.40.5_n5_NbFit0 from training data based on testing data
#pred0 <- predict(DryBean_TDA_KDE_5.40.5_n5_NbFit0, newdata= Dry_Bean_DatasetTest)

# Create confusion matrix to assess model fit/performance on test data
#nb_tda_kde_5.40.5_n5_db_nb_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
#nb_tda_kde_5.40.5_n5_db_nb_cf0
#nb_tda_kde_5.40.5_n5_db_nb_cf0 
#nb_tda_kde_5.40.5_n5_db_nb_cf0$overall
#nb_tda_kde_5.40.5_n5_db_nb_cf0_ov_acc<-nb_tda_kde_5.40.5_n5_db_nb_cf0$overall[1]
#nb_tda_kde_5.40.5_n5_db_nb_cf0$byClass
#nb_tda_kde_5.40.5_n5_db_nb_cf0_pre_rec_f1<-nb_tda_kde_5.40.5_n5_db_nb_cf0$byClass[5:7]

###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers

### 3-fold diff

#diff_drybean_tda_kde_5.40.5_nb_n5_3_fold<-(db_nb_fit_re - nb_tda_kde_5.40.5_n5_nb_fit_re)
#diff_drybean_tda_kde_5.40.5_nb_n5_3_fold

## Bayesian Tests 3-fold diff

# Bayesian Sign Test

#bst_tda_kde_5.40.5_nb.n5_3_fold<-#BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_nb_n5_3_fold),-0.01,0.01)
#bst_tda_kde_5.40.5_nb.n5_3_fold

# Odds Left Bayesian Sign Test 

#bst_tda_kde_5.40.5_nb.n5_3_fold_odds.left<-bst_tda_kde_5.40.5_nb.n5_3_fold$probLeft/#bst_tda_kde_5.40.5_nb.n5_3_fold$probRight
#bst_tda_kde_5.40.5_nb.n5_3_fold_odds.left

# Bayesian Signed Rank Test

#bsr_tda_kde_5.40.5_nb.n5_3_fold<-#BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_nb_n5_3_fold),-0.01,0.01)
#bsr_tda_kde_5.40.5_nb.n5_3_fold

# Bayesian Correlated Test

#bct_tda_kde_5.40.5_nb.n5_3_fold<-#correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_nb_n5_3_fold),0.1,-0.01,0.01)
#bct_tda_kde_5.40.5_nb.n5_3_fold

# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_nb_n5_3_fold,c(-0.01,0.01)))

#BayesFactor
#bf_tda_kde_5.40.5_nb.n5_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_nb_n5_3_fold))
#bf_tda_kde_5.40.5_nb.n5_3_fold

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_nb_n5_3_fold))


### Test set diff
#diff_drybean_tda_kde_5.40.5_nb.n5_test<-(db_svm_cf_ov_acc - nb_tda_kde_5.40.5_n5_db_nb_cf0_ov_acc)
#diff_drybean_tda_kde_5.40.5_nb.n5_test


## Bayesian Tests Test set diff

# Bayesian Sign Test

#bst_tda_kde_5.40.5_nb.n5_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.40.5_nb.n5_test),-0.01,0.01)
#bst_tda_kde_5.40.5_nb.n5_test

# Odds Left Bayesian Sign Test 

#bst_tda_kde_5.40.5_nb.n5_test_odds.left<-bst_tda_kde_5.40.5_nb.n5_test$probLeft/#bst_tda_kde_5.40.5_nb.n5_test$probRight
#bst_tda_kde_5.40.5_nb.n5_test_odds.left

# Bayesian Signed Rank Test

#bsr_tda_kde_5.40.5_nb.n5_test<-#BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.40.5_nb.n5_test),-0.01,0.01)
#bsr_tda_kde_5.40.5_nb.n5_test

# Bayesian Correlated Test

#bct_tda_kde_5.40.5_nb.n5_test<-#correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.40.5_nb.n5_test),0.1,-0.01,0.01)
#bct_tda_kde_5.40.5_nb.n5_test

# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.40.5_nb.n5_test)))

#BayesFactor
#bf_tda_kde_5.40.5_nb.n5_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.40.5_nb.n5_test)) #bf_tda_kde_5.40.5_nb.n5_test

#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.40.5_nb.n5_test))